Method and system for determining spatial coordinates of a 3D reconstruction of at least part of a real object at absolute spatial scale

ABSTRACT

Determining spatial coordinates of a 3D reconstruction includes obtaining, from a first camera system, a first image comprising a first real object, obtaining, from a second camera system, a second image comprising a second real object associated with known geometric properties, wherein the first camera system and the second camera system have a known spatial relationship, and determining a scale of the face based on the second image and the known geometric properties of the at least part of the second real object. Determining the spatial coordinates of the 3D reconstruction also includes determining a pose of the first and second camera systems, and determining the spatial coordinates based on the pose of the first camera system and the scale of the at least part of the second real object.

BACKGROUND

The present disclosure is related to a method and system for determiningspatial coordinates of a 3D reconstruction of at least part of a firstreal object at absolute spatial scale.

Computer vision methods that involve analysis of images are often used,for example, in navigation, object recognition, 3D reconstruction, andAugmented Reality applications, to name a few. The images may becaptured by a single camera or different cameras. Detection of imagefeatures (such as corners and edges) and image feature extraction is acommon step in various computer vision methods or algorithms, such asimage based recognition, image based tracking, image basedreconstruction, image based classification, and image warping. Forexample, vision based Simultaneous Localization and Mapping (SLAM) is awell-known computer vision method using one or more cameras forreconstructing a real environment and tracking the one or more cameras.Given at least two images captured by one or more cameras, a typicalSLAM method comprises feature detection, description, matching,triangulation and (global) map refinement.

It is a commonly known problem that approaches to determine thestructure of a real object based on a set of images captured by amonocular capture apparatus result in a reconstruction of the spatial(or geometrical) structure which is up-to-scale. This means thereconstruction uses spatial units for which the scaling factor toabsolute spatial units, such as the unit meter, is unknown. In manyapplications, it is desirable to obtain, e.g. a reconstruction inabsolute units, also referred to as “at absolute scale”. This oftenrequires knowledge of at least one distance at absolute scale, forexample between parts of the real object or between positions of thecamera relative to the real object at the time when the respectiveimages for reconstruction were taken.

Thus, a common problem of various SLAM and SfM systems is that areconstructed geometrical model of a real environment is up to a scaleas an undetermined factor. If the real object is unknown and the posesof the cameras that took the images for reconstruction are also unknown,then it is impossible to determine the absolute spatial scale of thescene. For example, based on two images of a car as shown in FIG. 2a—one taken from the front I(W1), and one from the right I(W2)—it isimpossible to tell if it is a real full-size car or if it is a smallrealistic miniature car. Consequently, it is also impossible to tell ifthe cameras that took the two images are many meters apart from another(as is the case for a full-size car) or only a few centimeters apart (asis the case for a miniature car). However, if additional information onthe absolute spatial scale of either the camera poses (e.g. the twocameras are 2.34 meters apart) or parts of the object (e.g. the car'sheadlights are 3.45 centimeters apart) is known, the reconstruction canbe performed at absolute scale.

In a case where the absolute spatial scale of a scene cannot bedetermined, the SLAM system may assign a random scale for example bydetermining initial keyframes from pixel disparity measurements in imagespace and assuming some generic real-world distance for the baselinebetween the two corresponding camera poses. Therefore, reconstructed 3Dfeatures have coordinates in a coordinate system associated with thegeometrical model which has an unknown scale factor relative to absolutecoordinates as they are in the real world, e.g. millimeters,centimeters, meters, or inches. Further, camera positions computed basedon the recovered geometrical models are also up to the scale, seereference [4].

The undetermined scale factor introduces challenges to determine truecamera movements at absolute scale in, for example, vision-basednavigation of a robot system or a vehicle, and to correctly overlayvirtual visual information to the real environment in an image of acamera in Augmented Reality applications. As an example, a vision-basednavigation application may be able to determine the shape of the cameramotion (e.g. that the camera is moving on a circular path), but itcannot determine translational parts (e.g. distances or positions) atabsolute scale, e.g. if the radius of the circle is 1 meter or 10meters. As another example, consider an Augmented Reality applicationthat superimposes a virtual piece of furniture spatially registered on alive video feed of the environment. If camera tracking is performed in acoordinate system with a random (i.e. arbitrary) scale, then also thesuperimposed virtual furniture will have an arbitrary scale. A virtual 2meters high cupboard could look three times as high as a 1 meter hightable or it could look half as high as that table, depending on thearbitrary scale that was chosen during reconstruction. Obviously, thisis not desirable. Instead, a virtual 2 meters high cupboard shouldappear twice as high as a 1 meter high real table next to it. The realand the virtual objects in the camera augmented by superimpositionshould be consistent in terms of scale. In order to enable this, the(correct) absolute scale of the geometrical model of the realenvironment is desired to be known.

Also, in a situation in which multiple geometrical models of multiplereal objects have been separately created using the same vision-basedSLAM system for tracking the multiple real objects simultaneously, likein reference [8], the problem of undetermined scale factors is quitesignificant. Typically, random scale values are applied to each of themultiple geometrical models. If the SLAM system switches between thegeometrical models, the scale may change and, therefore, the userexperience in computer vision applications like Augmented Reality isseriously affected.

Various methods have been proposed for determining correct scale factorsthat could define true sizes of reconstructed geometrical models of realenvironments as they are in the real world.

For example, Davison et al. in reference [1] propose to introducecalibration objects with known absolute spatial dimensions into thescene for determining absolute scale in SLAM systems. Thereby they needto change the appearance of the scene because they use the same camerato capture the calibration objects and to capture the scene toreconstruct in SLAM. Also the user has to have the calibration objectsavailable.

Lemaire et al. in reference [5] propose to use a stereo camera system(i.e. two cameras with displacement with an overlapping camera frustum)to solve the problem of determining absolute scale in SLAM systems.However, using a stereo camera is only a partial remedy, since thedisplacement between the two cameras has to be significant in relationto the distance to the environment or object in order to reliablycompute depth of the environment. Also the displacement between the twocameras needs to be known at absolute scale, i.e. in units such asmillimeters, centimeters, meters, or inches.

Also approaches for estimating absolute scale using multi-camera set-upswith non overlapping camera frustums are disclosed in reference [14].However, the displacement between the two cameras has to be significantin relation to the distance to the environment or object in order toreliably compute depth of the environment.

Lieberknecht et al. in reference [6] integrate depth information intomonocular vision-based SLAM to allow correctly scaled geometrical modelreconstruction by employing an RGB-D camera that provides absolute depthinformation related to image pixels. It is possible to determineabsolute scale from known depth information at absolute scale. However,an RGB-D camera device is not commonly available in a hand-held device,e.g. mobile phone, tablet computer, or PDA, compared to a normalmonocular RGB camera. Also active stereo-based depth cameras, that arebased on projecting infrared light into the scene, do not work reliablyif there is significant infrared environment light, as the case foroutdoor environment during daylight.

Klein et al. in reference [7] solve the problem of scale estimation bymanually defining a baseline (i.e. the distance at absolute scale)between the two positions of a camera while it captured the two imagesneeded for 3D triangulation, which is used to reconstruct theenvironment.

Sensor fusion with an Inertial Measurement Unit (IMU) could also be usedto estimate the absolute scale, as disclosed in reference [9]. Oneproblem with this approach is the inaccuracy of the sensor valuesresulting in inaccurate scale estimates. Expensive (i.e. calculationintensive) techniques like “Kalman Filtering” or “Bundle Adjustment” areused to address the problem, but usually the accuracy of the IMUsintegrated in off-the-shelf devices, such as mobile phones, is notsufficient to estimate absolute scale accurately.

Therefore it would be desirable to provide a method and system fordetermining spatial coordinates of a 3D reconstruction of at least partof a first real object at absolute spatial scale which are capable ofreconstructing real objects at absolute scale or determining a scalefactor which maps coordinates of a reconstruction at an arbitrary scaleto absolute scale.

SUMMARY

According to a first aspect, there is disclosed a method of determiningspatial coordinates of a 3D reconstruction of at least part of a firstreal object at absolute spatial scale comprising the steps of:

a) receiving image information of a first image including at least partof the first real object captured with a first camera

b) receiving image information of a third image including the at leastpart of the first real object captured with a third camera,

c) receiving image information of a second image including at least afirst part of a second real object captured with a second camera,

d) receiving image information of a fourth image including at least asecond part of the second real object captured with a fourth camera,

wherein the frustum of the first camera and the frustum of the secondcamera do not overlap and wherein the frustum of the third camera andthe frustum of the fourth camera do not overlap,

e) providing a first spatial transformation between the first camera andthe second camera and providing a second spatial transformation betweenthe third camera and the fourth camera,

f) providing a first scale information indicative of an absolute spatialscale of the at least first part of the second real object, and a secondscale information indicative of an absolute spatial scale of the atleast second part of the second real object,

g) determining at least part of a pose of the second camera and at leastpart of a pose of the fourth camera according to the second image, thefirst scale information, the fourth image and the second scaleinformation, wherein the at least part of the pose of the second cameraand the at least part of the pose of the fourth camera are defined in asecond common coordinate system,

h) determining at least part of a pose of the first camera according tothe at least part of the pose of the second camera and the first spatialtransformation, and determining at least part of a pose of the thirdcamera according to the at least part of the pose of the fourth cameraand the second spatial transformation, wherein the at least part of thepose of the first camera and the at least part of the pose of the thirdcamera are defined in the second common coordinate system,

i) determining spatial coordinates of a 3D reconstruction of the atleast part of the first real object at absolute spatial scale accordingto the first image, the third image and the at least part of the pose ofthe first camera and the at least part of the pose of the third camera.

According to an embodiment, the at least part of the pose of the secondcamera, the at least part of the pose of the fourth camera, the at leastpart of the pose of the first camera, and the at least part of the poseof the third camera each include translational information.

According to an embodiment, the step i) comprises determining the poseof the first camera and the pose of the third camera according to thefirst image and the third image, wherein the pose of the first cameraand the pose of the third camera are defined in a first commoncoordinate system, determining a first translational informationindicative of at least one distance according to translationalinformation of the pose of the first camera and translationalinformation of the pose of the third camera in the first commoncoordinate system, determining a second translational informationindicative of at least one distance according to the at least part ofthe pose of the first camera and the at least part of the pose of thethird camera in the second common coordinate system, determining a scalefactor according to the first and the second translational information,determining spatial coordinates of the 3D reconstruction of the at leastpart of the first real object at arbitrary spatial scale in the firstcommon coordinate system according to the first image, the third imageand the pose of the first camera and the pose of the third camera, andtransforming the determined spatial coordinates of the 3D reconstructionfrom the arbitrary spatial scale of the first common coordinate systemto the absolute spatial scale of the second common coordinate systemaccording to the scale factor.

According to an embodiment, the step i) further comprises determiningthe pose of the first camera and the pose of the third camera accordingto the first image and the third image, wherein the pose of the firstcamera and the pose of the third camera are defined in a first commoncoordinate system, providing the at least part of the pose of the firstcamera and the at least part of the pose of the third camera defined ina second common coordinate system, updating translational informationrelated to the pose of the first camera and translational informationrelated to the pose of the third camera in the first common coordinatesystem according to the at least part of the pose of the first cameraand the at least part of the pose of the third camera in the secondcommon coordinate system, and determining the spatial coordinates of the3D reconstruction of the at least part of the first real object atabsolute spatial scale according to the first image, the third image andthe updated pose of the first camera and the updated pose of the thirdcamera.

For example, the at least part of the pose of the second camera includes3 DoF translational information and 3 DoF rotational information, the atleast part of the pose of the fourth camera includes 3 DoF translationalinformation and 3 DoF rotational information, the at least part of thepose of the first camera includes 3 DoF translational information and 3DoF rotational information, and the at least part of the pose of thethird camera includes 3 DoF translational information and 3 DoFrotational information.

According to a second aspect, there is disclosed a method of determiningspatial coordinates of a 3D reconstruction of at least part of a firstreal object at absolute spatial scale comprising the steps of:

a) receiving image information of a first image including at least partof the first real object captured with a first camera,

b) receiving image information of a third image including the at leastpart of the first real object captured with a third camera,

c) receiving image information of a second image including at least afirst part of a second real object captured with a second camera,

d) receiving image information of a fourth image including at least asecond part of the second real object captured with a fourth camera,

wherein the frustum of the first camera and the frustum of the secondcamera do not overlap and wherein the frustum of the third camera andthe frustum of the fourth camera do not overlap,

e) providing a first scale information indicative of an absolute spatialscale of the at least first part of the second real object, and a secondscale information indicative of an absolute spatial scale of the atleast second part of the second real object,

f) determining at least part of a pose of the second camera and at leastpart of a pose of the fourth camera according to the second image, thefirst scale information, the fourth image and the second scaleinformation, wherein the at least part of the pose of the second cameraand the at least part of the pose of the fourth camera are defined in asecond common coordinate system, wherein the at least part of the poseof the second camera includes translational information and the at leastpart of the pose of the fourth camera includes translationalinformation,

g) determining the pose of the first camera and the pose of the thirdcamera according to the first image and the third image, wherein thepose of the first camera and the pose of the third camera are defined ina first common coordinate system,

i) the method further comprising

-   -   i0) determining a second translational information indicative of        at least one distance according to the at least part of the pose        of the second camera and the at least part of the pose of the        fourth camera,    -   i1) determining a first translational information indicative of        at least one distance according to a translation of the pose of        the first camera and a translation of the pose of the third        camera,    -   i2) determining a scale factor according to the first and the        second translational information,    -   i3) determining spatial coordinates of a 3D reconstruction of        the at least part of the first real object at arbitrary scale        according to the first image, the third image and the pose of        the first camera and the pose of the third camera,    -   i4) transforming the determined spatial coordinates of the 3D        reconstruction from the arbitrary spatial scale to absolute        spatial scale according to the scale factor,    -   or

k) instead of steps i0)-i4), the method further comprising

-   -   k1) updating translational information related to the pose of        the first camera and translational information related to the        pose of the third camera to be at absolute spatial scale        according to the at least part of the pose of the second camera        and the at least part of the pose of the fourth camera,    -   k2) determining spatial coordinates of a 3D reconstruction of        the at least part of the first real object at absolute spatial        scale according to the first image, the third image and the        updated pose of the first camera and the updated pose of the        third camera.

The following embodiments may be implemented equally for the inventionaccording to the first aspect or the second aspect.

According to an embodiment, the second common coordinate system isassociated with the second real object, and the step g) (first aspect)or f) (second aspect), respectively, comprises determining the at leastpart of the pose of the second camera according to the second image andthe first scale information, and determining the at least part of thepose of the fourth camera according to the fourth image and the secondscale information.

According to an embodiment, the second common coordinate system isassociated with a selected camera which is one of the second camera andthe fourth camera, wherein another camera of the second camera and thefourth camera is an unselected camera, and the step g) (first aspect) orf) (second aspect), respectively, comprises providing a pose related tothe selected camera in the second common coordinate system, anddetermining a pose related to the unselected camera according to thesecond image, the first scale information, the fourth image and thesecond scale information.

According to an embodiment, the first common coordinate system isassociated with the first real object or one of the first camera and thethird camera.

According to an embodiment, the at least first part of the second realobject and the at least second part of the second real object are thesame part and the first scale information and the second scaleinformation are the same, or the at least first part of the second realobject and the at least second part of the second real object aredifferent parts and the first scale information and the second scaleinformation are different.

According to an embodiment, the first camera and the second camera facein opposite directions, and the third camera and the fourth camera facein opposite directions.

According to an embodiment, the second real object is a human face, andthe step g) (first aspect) or f) (second aspect), respectively, isimplemented with a face tracking method, wherein the first scaleinformation and/or the second scale information is provided as at leastone distance between positions of at least two facial features atabsolute scale.

According to an embodiment, the second real object is a human face, andthe step g) (first aspect) or f) (second aspect), respectively, isimplemented with a face tracking method and wherein the first scaleinformation and/or the second scale information is provided as at leastone depth information at absolute scale for the position of at least onepart of the second real object in at least one of the second and fourthimages.

According to an embodiment, at least one of the second and fourth cameraprovide depth information at absolute scale and the step g) (firstaspect) or f) (second aspect), respectively, is implemented with avisual odometry method at absolute scale and the first scale informationand/or the second scale information is provided by the depthinformation.

According to an embodiment, the first camera and the third camera arethe same camera at different points in time, and the second camera andthe fourth camera are the same camera at different points in time.

According to an embodiment, the second real object is a human face.

According to an embodiment, the first scale information and/or thesecond scale information comprises the interpupillary distance.

According to an embodiment, the method further includes providing atleast part of intrinsic camera parameters of the second camera and atleast part of intrinsic camera parameters of the fourth camera.

Preferably, a user who carries the first to fourth camera is static. Forexample, the method further includes the step of determining when theuser is static.

According to an embodiment, the second and fourth camera each comprisean infrared camera.

According to an embodiment, the determination of the scale factor isbased on a set of N pairs of poses, with N>2.

According to an embodiment, the method further uses a human face modelwith respect to the second real object which is generic.

According to an embodiment, the method further uses a human face modelwith respect to the second real object which is probability distributionbased.

According to an embodiment, the method further uses a human face modelwith respect to the second real object which is calibrated, particularlyadaptive, reconstructed, or manually measured.

According to an embodiment, the method further comprises facerecognition or classification to choose a model related to the secondobject.

According to an embodiment, the method further comprises detecting auser input for starting the method and providing an instruction to theuser to perform a certain motion with the second camera, the motionbeing measured based on images captured with the second camera, andreceiving a user interaction which triggers the determination of thespatial coordinates of the 3D reconstruction.

According to an embodiment, the second real object is considered beingstatic in relation to the first real object while capturing the first,second, third, and fourth image.

According to an embodiment, the first, second, third, and fourth imageare selected based on a method that determines that the second realobject is static in relation to the first real object while capturingthe first, second, third, and fourth images.

According to an embodiment, determining a second translationalinformation indicative of at least one distance according to the atleast part of the pose of the second camera and the at least part of thepose of the fourth camera considers only the distance between the secondreal object and the second camera and the distance between the secondreal object and the fourth camera.

According to an embodiment, the steps a) to i2) of the second aspect areperformed repeatedly on a multitude of sets of first images, secondimages, third images, and fourth images, wherein the images of thedifferent sets may or may not overlap with each other, resulting in amultitude of scale factors in step i2), and the method furthercomprising determining from the multitude of scale factors a singlescale factor, and in step i4) using the single scale factor to transformthe spatial coordinates.

According to an embodiment, the steps a) to i1) of the second aspect areperformed repeatedly on a multitude of sets of first images, secondimages, third images, and fourth images, where the images of thedifferent sets may or may not overlap with each other, resulting in amultitude of first translational information in step i1) and secondtranslational information in step i0), wherein step i2) determines ascaling factor according to the multitudes of first translationalinformation in step i1) and second translational information in stepi0).

According to an embodiment, the first scale information and/or thesecond scale information is provided as at least one distance betweenthe position of at least two facial features at absolute scale accordingto a generic face model, e.g. probability distribution based.

According to an embodiment, the first scale information and/or thesecond scale information is provided as at least one distance betweenthe position of at least two facial features at absolute scale accordingto a previous calibration, e.g. by adaptive fitting, reconstruction,manually measuring of at least one distance between the position of atleast two facial features at absolute scale of a particular face.

According to an embodiment, the first scale information and/or thesecond scale information is provided as at least one distance betweenthe position of at least two facial features at absolute scale accordingto a model selected based on visual face recognition.

According to an embodiment, the first scale information and/or thesecond scale information is provided as at least one distance betweenthe position of at least two facial features at absolute scale accordingto a model selected from a database based on visual face classification,to determine properties of the face such as the age, gender, ethnicity,weight, or height from a dataset of generic face models for differentclasses of faces.

According to an embodiment, the 3D reconstruction of the first realobject at absolute scale is used to determine a camera pose at absolutescale. For example, the camera pose at absolute scale is used tosuperimpose digital information in an augmented reality application.

According to an embodiment, the method is being triggered by a userinput. According to another embodiment, the method is being triggeredautomatically.

According to another aspect, there is disclosed a system for determiningspatial coordinates of a 3D reconstruction of at least part of a firstreal object at absolute spatial scale, comprising a processing systemwhich is configured to perform the steps as set out in any of theaspects and embodiments disclosed above.

According to a preferred embodiment, when having a capturing apparatusthat captures a human face and a real object, we thereby use the face todetermine a distance at absolute scale, which may be used for thepurposes of the present invention. The capturing apparatus can be asingle camera or a set of rigidly connected cameras, e.g. such as in acommonly available mobile phones. With such mobile phone having a frontfacing camera and a back facing camera, the front facing camera maycapture the user's face while the back facing camera captures the(first) real object.

Generally, the invention is related to the problem of determiningspatial coordinates of a 3D reconstruction of a first real object atabsolute scale, which may be addressed by observing the first realobject and a second real object with an imaging device from each atleast two viewpoints where the spatial coordinates of at least twopoints of the second real object are known at absolute scale, whichenables determining the distance between the at least two viewpoints atabsolute spatial scale, and which enables determining absolute spatialscale for the spatial coordinates of the reconstruction of the firstreal object.

Further, the inventors found that the face of the user can be capturedby a user-facing camera while capturing an object or environment with aworld-facing camera. It further includes realizing that human faces havea limited variety in absolute scale and therefore provide a good scaleconstraint for measuring at absolute scale. A generic face model can beused to determine absolute spatial scale if the spatial properties of aparticular user's face are unknown. Thereby an introduced error dependson the variation of the spatial properties of human faces that led tothe estimate. Even if the assumed spatial properties differ from theactual ones, which results in a somewhat more inaccurate absolute scaleestimate, multiple scale estimates using the same properties and thesame face will always result in similar absolute scales. Thereby, scaleestimated may be inaccurate but precise, i.e. consistent. If aparticular face is used that has been calibrated (i.e. measured), thenthe scale of the face is accurately known at absolute scale.

Further, it has been discovered that no extra known object needs to beadded to the scene. By exploiting a user-facing and a world-facingcamera of modern handheld devices, a human face which providesinformation about absolute scale does not need to be part of that partof the scene which is captured and reconstructed by the world-facingcamera, but instead can be captured while a user is operating anapplication. As a result, the user's face does not become part of thereconstruction of the scene, as opposed to approaches that add a markeror known object to the scene which is then being reconstructed as partof the scene.

The method described in this invention may be used, for example, inrelation with vision-based Simultaneous Localization and Mapping (SLAM),such as disclosed in reference [1], which is a well-known technology forcreating a geometrical model of a real environment using one or morecameras without requiring any pre-knowledge of the environment. Anothercommon term for the same technology is Structure from Motion (SfM). Thegeometrical model, that has at least depth information, is also referredto as a 3D map of the real environment. The creation of the geometricalmodel of the environment is also called (3D) reconstruction of the realenvironment. The created (or typically called reconstructed) geometricalmodel could be represented by a plurality of 3D features, such as pointfeatures or edge features. The 3D features describe physical 3D features(also referred to as the structure) of the real environment. A realenvironment may also be called a real scene, a real object, or may beunderstood to comprise one or more real objects.

The reconstructed geometrical model can be used in differentapplications. For example it can be visualized on a display, or printedwith a three-dimensional printer. It can also serve as a basis tomeasure spatial distances between points on the real object.

The reconstructed geometrical model can also be used for determining apose (i.e. position and/or orientation) of a camera relative to the realobject based on a current image the camera captured of the real object.By matching extracted 2D features of the current camera image with 3Dfeatures existing in the geometrical model, e.g. by means of local imagefeature descriptors (reference [20]), a plurality of 2D-3Dcorrespondences can be established. Then, the camera position andorientation in a coordinate system of the geometrical model can becomputed based on the correspondences. This procedure is referred to ascamera pose estimation and sometimes also referred to as tracking acamera. The problem of tracking a camera relative to a real object canalso be expressed as the problem of tracking a real object relative tothe camera. If one of the two problems has been solved, the solution ofthe second problem is the inverse of the solution of the first problem.Therefore tracking a camera and tracking an object can be usedinterchangeably when discussing the overall concept.

Vision-based SLAM performs camera tracking and reconstruction of theenvironment in parallel. It facilitates many applications, such asvision-based navigation of a robot system or a vehicle. Particularly, itis a promising technology that supports Augmented Reality (AR) systemsor applications (see reference [3]) in an unknown real environment.

An aim of the invention is to determine the absolute scale of areconstructed geometrical model of a real environment such that thecoordinate system of the geometrical model is at absolute scale, meaningthere is a known scaling factor mapping from coordinate system units ofthe reconstructed geometrical model to absolute spatial units as theyare in the real world. For example the model can be scaled tomillimeters, such that a model unit in the model corresponds to amillimeter on the real object. In this case, if two points in the modelare 56 units apart, the corresponding points on the real object are 56mm away from each other. Defining a model at absolute scale can beimplemented such that a unit in the model coordinate system correspondsto any real-world distance (e.g. 12.34 meters) as long as this distanceis known.

Thus, at least one scale factor may be determined that could be used toscale a plurality of 3D features defined in a coordinate systemassociated with a geometrical model describing a real object.

In one embodiment, the present invention determines a scale factor whichscales the coordinates of an existing model of a real object defined atarbitrary scale to a coordinate system which is defined at absolutescale.

In another embodiment, the present invention determines the distancebetween at least two cameras at absolute scale, which then enablesreconstructing a 3D model at absolute scale of a real object visible inthe camera based on images of the two cameras.

We may determine the scale factor between the spatial coordinate systemwhere the spatial properties of the features are defined in and a realworld metric, e.g. centimeters. If the coordinate system in which theposition of features is defined is already given, the scale factor canbe potentially used to scale the coordinate system (and the spatialcoordinates of the features respectively) to have a one-to-one scalingto a real-world metric (like one unit=one mm). If the coordinate systemin which the position of features is defined is not yet determined, wecan potentially use the scale factor to directly initialize thecoordinate system (and the spatial coordinates of the featuresrespectively) to have a one-to-one scaling to a real-world metric (likeone unit=one millimeter).

Further, the coordinate system can also be kept as is. For operationsthat require the absolute scale (i.e. representing the real-worldscale), the determined spatial scale factor then can be used forextracting real-world spatial distances or transform poses or spatialcoordinates of features to a coordinate system that has a one-to-onescaling to a real-world metric.

In another embodiment, two poses at absolute scale are provided for twoimages of a first camera, e.g. world-facing camera at two points intime, used for reconstruction of the scene structure by triangulation.The two poses at absolute scale could be provided by a face trackingmethod based on images captured by a different camera facing the user,in the following referred to as user-facing camera, with a known spatialtransformation with respect to the first camera.

In many potential scenarios, a user holds a mobile device equipped witha world-facing camera. The world-facing camera may be used to captureimages of the surrounding environment, for example to superimpose thesewith virtual objects in Augmented Reality applications or for otherimage processing applications. In such applications, it is oftenrequired to reconstruct a real object in the surrounding environmentand/or to estimate camera poses or motions of the camera relative to thereal object or the environment.

In a scenario such as illustrated in FIG. 6 below, where we have anadditional front-facing (also referred to as user-facing) camera, we canuse an image of the face of a user, who operates the application and istherefore already present in the real environment, to estimate theabsolute scale of the map (i.e. 3D reconstruction) created based onimages captured with the world-facing camera.

This has various advantages: The face of the user is available, so noextra geometry or object has to be added, and it is captured by theuser-facing camera, so no tedious setup is required. Since the usertypically looks at the screen in order to experience the application,the user-facing camera can always capture the face of the user, whilethe world-facing camera can capture the view of the real environment. Asthe face of the user is always available as long as the user is facingor looking at the display of the handheld device, dynamically updatingor redoing the scale estimation can be supported. The geometry of ahuman face is also limited in range of variation in geometry and therebyallows valid assumptions and restrictions about the dimensions and thescale of facial features for the majority of humans. This means, thescale estimation can be done by everyone without the need of anadditional known object using only the user's face and the capturedevice. A particular user can also do a special calibration for his orher own face, allowing for a higher precision. A face recognitionprocedure, which allows to distinguish between a multitude of people,e.g. in reference [19], can also be incorporated to recognize which useris present in the image of the user-facing camera, and then selectingthe according correct absolute dimensions of the user's face from apreviously set-up database. The face recognition procedure can eitherrun locally or be executed remotely accessible through networkconnection. The previously set-up database containing correct absolutedimensions of the user's face can be either provided locally orremotely, accessible through network connection.

Another embodiment uses a visual face classification method, e.g. asdisclosed in reference [12], to determine properties such as the age,gender, ethnicity, weight, or height of humans and then uses a genericface model for the determined class of humans.

The two cameras (e.g., world-facing and user-facing camera) may be usedin combination with the assumption of a known spatial relation betweenthe coordinate systems of the two cameras, e.g. a rigid bodytransformation. The world-facing camera may be used for determining apose of the world-facing camera relative to a real environment in acoordinate system associated with the real environment and/or an objectcoordinate system associated with a real object located in the realenvironment. This would allow a desired alignment between virtualobjects that can be superimposed on the camera image and the real objectin the real environment in the image captured by the world-facingcamera. Assuming the known transformation between the coordinate systemsof the two cameras, the absolute scale information from the face of theuser captured by the user-facing camera can be transformed into the realenvironment coordinate system and/or the object coordinate system. Thiswould allow SLAM reconstruction at absolute scale using the world-facingcamera.

For each viewpoint of a camera setup comprising a user-facing camera anda rigidly connected world-facing camera, we can determine a pairconsisting of two poses: the pose of the user-facing camera relative tothe user's face at absolute spatial scale, and the pose of theworld-facing camera relative to the first real object at arbitraryscale. Given the spatial transformation between the user-facing cameraand the world-facing camera, we can determine the pose of theworld-facing camera relative to the user's face at absolute spatialscale by transforming the pose of the user-facing camera relative to theuser's face at absolute spatial scale with the spatial transformationbetween the user-facing camera F and the world-facing camera.

Given two such transformed poses, resulting from two differentviewpoints of the dual camera setup, we can determine the translationaldistance D_abs between the two poses of the world-facing camera atabsolute scale. Using the two poses of the world-facing camera relativeto the first real object defined at arbitrary scale, we can determinethe translational distance D_arb between these two poses at arbitraryscale.

Finally, a scaling factor from the arbitrary scale of the coordinatesystem relative to the real object to absolute scale can be determinedas the ratio of D_abs and D_arb. S=D_abs/D_arb.

Without referring to the figures, determining the absolute spatialdistance between two camera poses PW1 and PW2 of a first camera WC(capturing images of at least part of a real-world object for a SLAMreconstruction) belonging to a capture apparatus C by observing aspatial translation and rotation of a second camera FC (capturing imagesof at least part of a human face) belonging to the same captureapparatus C using image-based camera pose estimation relative to a facefor at least two images, where at least one image I(F1) of the face istaken at camera pose PF1 of camera FC, which means that camera WC atthis point of time was in camera pose PW1, and another image I(F2) ofthe face is taken at camera pose PF2 of camera FC, which means thatcamera WC at this point of time was in camera pose PW2. At least part ofa real-world object may be captured in the images I(W1) and I(W2) by thecamera WC at the camera pose PW1 and PW2 respectively. The images I(W1)and I(W2) may be used for real object reconstruction or camera poseestimation applications (e.g. SLAM). The absolute spatial distancebetween two camera poses PW1 and PW2 should be non-zero for the methodto determine spatial properties of the real object at absolute scale.

In one embodiment the scale estimation is not only based on twocorresponding pairs of poses PW and PF (i.e. PW1 and PF1 as well as PW2and PF2) determined by the corresponding captured four images at theseposes (like basically illustrated in FIGS. 2a and 2b ), but on amultitude of pairs of poses PW and PF, each of which is determined by acaptured image. Multiple scale estimates each based on two pairs ofposes (W_i and F_i as well as W_j and F_j) can be combined using forexample a model fitting method such as median, mean or RANSAC. The modelfitting method may additionally consider suitability of certain posepairs for the scale estimation, for example based on a minimum distancebetween the poses or uncertainty and quality ratings of the posemeasurements. Also the coherency between the difference from F_i to F_jin rotation of the user-facing camera and the difference from W_i to W_jin rotation of the world-facing camera can be used for example as arating for uncertainty and/or quality of a pose measurement and anindicator for whether the second real object has moved with respect tothe first real object or not. The rotational part between the two posesof the user-facing camera can also be used to determine if the poses aresuitable for the scale estimation. When neglecting the realtransformation between user-facing and world-facing camera, and assumingthey have the same origin, a rotational part between the two poses ofthe user-facing camera may introduce an error for the scale estimation(see FIGS. 8a, 8b, and 8c ) and it is therefore preferable to only havenegligible/small rotations. The two whole trajectories of poses (onetrajectory for the user-facing camera, one for the world-facing camera)can also be used to evaluate, how likely it is that the face has notmoved during the capturing. This can for example be evaluated using amethod as disclosed by Umeyama (reference [10]) aligning the twotrajectories and computing the residual error after registration. If theresidual error is above a particular threshold, this may be indicativeof that the head moved relative to the real object. In this case, adetermined scale factor may be discarded and calibration may berestarted. Also the coherency between the rotation of the user-facingcamera and the rotation of the world-facing camera can be used tosupport aligning the two trajectories and be considered when computingthe residual error.

Modern handheld and mobile devices, such as mobile phones, pads, ortablet computers, may have two equipped cameras (e.g. a user-facingcamera and a world-facing camera) pointing into two opposite directions.The display of the mobile device usually faces in the same direction asthe user-facing camera does.

A possible embodiment of the invention is estimating absolute scale fromimages of the face of the user captured by the user-facing camera. Thisabsolute scale is then applied for the reconstruction and for trackingreal objects at absolute scale using another camera (e.g. a world-facingcamera that points to the opposite direction compared to the user-facingcamera and usually has a known spatial transformation relative to theuser-facing camera). The two cameras may be attached to a handhelddevice or a mobile device, e.g. a mobile phone, a pad, or a tabletcomputer. Further, a display device, e.g. an LCD screen, may be attachedto the mobile device.

The two cameras of a mobile device may have a fixed spatialrelationship, e.g. a rigid body transformation, which may be determinedfrom a calibration procedure, e.g. hand-eye calibration, by using atleast one known marker or an additional tracking system.

Common approaches in the state of the art require special cameras (withdepth sensors based on active stereo or passive stereo ortime-of-flight) or additional setups in order to estimate the absolutescale of a real object. This definitely restricts the applicability ofthese approaches.

One approach of estimating the absolute scale of a SLAM map of a realscene is to detect a known object directly in the images of the cameraused as input for the SLAM method (like described in reference [1]) anduse the known absolute scale of the known object to infer the absolutescale of the map. One problem of this approach is the necessity ofavailability of a known object as well as an additional setup step,wherein the extra known object is added to the scene. This changes theoriginal scene and requires the camera to be directed towards the knownobject.

Compared with state of the art using special objects like a planarmarker, like used in reference [1], that have to be placed explicitly inthe room and captured by a SLAM camera for the scale estimation, theface of the user has the great advantage that one need not pay specialattention to keep the object within the field of view of the user-facingcamera during the whole process of reconstruction.

Another significant difference of this invention compared to approachesbased on adding an object with known absolute spatial properties to thescene to be reconstructed (reference [1]) is that the present inventiondoes not rely on capturing the known object with the same camera that isused for reconstruction of the real object or scene, but instead uses asecond camera to capture the face. Thereby, the face does not becomepart of the reconstruction as opposed to real objects added to thescene.

As opposed to approaches such as in reference [1] that add a knownobject to the scene and thereby require a camera-equipped computer, auser, a real object to reconstruct, and an additional special knownobject for calibration that a user would need to carry around, thepresent invention in contrast only requires a camera-equipped computer,a user, and a real-object to reconstruct.

According to embodiments of the present invention, one significantadvantage in determining the absolute scale of a SLAM reconstruction isthe explicit use of the absolute distance between two or more facialfeatures or fiducials (e.g. a distance between the two eyes of the faceor a distance between an eye and the mouth of the face or a distancebetween the left and the right corners of an eye). These may berecognized in an image of the user captured by a user-facing camera(i.e. a camera pointing to the user or pointing to an image of the userreflected by one or more optical instruments, e.g. mirrors or opticlenses, for capturing the user's face) when the user is observing thedisplay device. This allows the application of a-priori knowledge abouthuman faces and their absolute spatial properties. The images of theface are typically captured by a user-facing camera and are used forestimating the absolute scale instead of relying on additional objectsof known geometry in the scene for scale estimation. By explicitly usingthe face (e.g. using face specific characteristics), which is mostly oralways available over the whole duration of the user observing thedisplay, the scale can be estimated at any time without taking influenceon the scene. Additionally by focusing on the face, which has a limitedrange of variation in terms of geometry between all humans, specializedalgorithms for estimating the scale from the face of the user can beapplied. Regions of the face particularly suited for estimating thescale can be pre-learned and/or pre-defined. These regions can beregistered in live tracking via established algorithms of face detectionand pose tracking. Regions of the face that could have a bad impact onthe scale estimation, e.g. because they differ significantly amongdifferent people in terms of size and shape, can be taken into accountand excluded from the scale estimation (like nose size, or ear size).

In one embodiment, the normal of the display device of the mobile deviceand the optical axis of the user-facing camera are preferred to have thesame direction. In this case, as the user would observe the visualinformation (e.g. of an augmented scene) on the display device, the faceof the user would mostly or always be captured by the user-facingcamera. Thus, the absolute scale could be always estimated based onimages of the face.

For example, the processing system according to the invention iscomprised, at least in part, in a mobile device (such as a mobile phone,wearable computer, tablet computer, mobile computer, often calledlaptop, or a head mounted display, such as used for optical see-throughaugmented reality applications) and/or in a server computer adapted tocommunicate with the mobile device. The processing system may becomprised in only one of these devices, e.g. in the mobile device or inthe server computer, or may be a distributed system in which one or moreprocessing tasks are distributed and processed by one or more processingdevices of the processing system which are distributed and arecommunicating with each other, e.g. by point to point communication orvia a network.

According to an embodiment, the system comprises a mobile device whichcomprises one or more cameras and, for example, a display screen.

Any steps, embodiments, aspects and examples described herein withrespect to the method can equally or analogously be implemented by theprocessing system being configured (by software and/or hardware) toperform the respective steps, embodiments, aspects or examples. Anyprocessing device used within the processing system may be configured assuch by software and/or hardware and communicate via a communicationnetwork, e.g. via a server computer or a point to point communication,with one or more cameras, displays and/or any other components.

According to another aspect, the invention is also related to a computerprogram product comprising software code sections which are adapted toperform a method according to the invention. Particularly, the softwarecode sections are contained on a computer readable medium which isnon-transitory. The software code sections may be loaded into the memoryof one or more processing devices (such as microprocessors) as describedherein. Any used processing devices, such as one or moremicroprocessors, may communicate via a communication network, e.g. via aserver computer or a point to point communication, as described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects and embodiments of the invention will now be described withrespect to the drawings, in which:

FIG. 1 shows a flowchart of a method according to an embodiment of thepresent invention,

FIGS. 2a and 2b illustrate a possible embodiment of the presentinvention and an ambiguity in scale when using monocular SLAM or SfM,

FIG. 3 shows another embodiment of the present invention,

FIG. 4 illustrates involved coordinate systems and transformationsaccording to embodiments of the invention,

FIG. 5 shows a capturing apparatus comprising a user-facing camera and arigidly attached world-facing camera at two different poses according toan embodiment of the invention,

FIG. 6 shows an exemplary embodiment of the present inventionimplemented with a handheld device,

FIG. 7 illustrates an example of a graphical user interface which guidesa user through an exemplary process of scale estimation,

FIGS. 8a, 8b, and 8c illustrate an influence of the spatialtransformation between the first and second camera,

FIG. 9 illustrates another embodiment of the present invention,

FIG. 10 illustrates another embodiment of the present invention,

FIG. 11 illustrates another embodiment of the present invention,

FIG. 12 illustrates another embodiment of the present invention.

DETAILED DESCRIPTION

It is a commonly known problem that approaches to determine thestructure of a real object based on a set of images captured by amonocular capture apparatus result in a reconstruction of the spatial(or geometrical) structure which is up-to-scale. This means thereconstruction uses spatial units for which the scaling factor toabsolute spatial units, such as meters, is unknown. In manyapplications, it is desirable to obtain a reconstruction in absoluteunits, also referred to as “at absolute scale”. For this, the knowledgeof at least one distance at absolute scale may be used, either betweenparts of the real object or between positions of the camera relative tothe real objects at the time when the respective images forreconstruction were taken. This distance at absolute scale could forexample be the eye distance either for a particular human or a genericeye distance or any other spatial property of facial fiducials. Whenhaving a capturing apparatus that captures a face and the real object,embodiments disclosed herein use the face to determine a distance atabsolute scale. The capturing apparatus can be a single camera or a setof rigidly connected cameras, e.g. such as in a mobile phone. There thefront facing camera usually captures the user's face while theback-facing camera captures the real object.

Advantageously, the invention takes advantage of the user's face (whichis not identical but has similar properties for most people), which maypreferably be used in handheld Augmented Reality.

The invention enables reconstruction of the structure of a real objector environment at absolute spatial scale, in the following also simplyreferred to as at absolute scale. This for example enables camera posetracking at absolute scale and it enables superimposing virtual objectswhich are defined at absolute scale to be at a consistent scale with thereconstructed real object. It also allows doing measurements of the realspace, enabling calculations that are based on absolute spatial scalelike physical simulations (e.g. acceleration due to gravity) orcollision detections between virtual and real objects (e.g. would aobject spatially fit into the real-word).

Instead of using a known object (i.e. marker) that needs to be added tothe scene, embodiments of the invention use the user's face, which isalways there. As opposed to other approaches, the invention does notrequire any user input, it does not require inertial sensors, andprovides more accurate results than consumer-grade inertial sensors do.

FIG. 1 shows a flowchart of a method according to an embodiment of thepresent invention. In a first step S1 a first image including at leastpart of the first real object captured with a first camera and a secondimage including at least part of a second real object captured with asecond camera and a third image including at least part of the firstreal object captured with the third camera and a fourth image includingat least part of the second real object captured with the fourth camerais provided. In a second step S2 a spatial transformation at absolutescale between the first camera and the second camera and a spatialtransformation at absolute scale between the third camera and the fourthcamera are provided. In a third step S3 information on the absolutespatial scale of at least part of the second real object is provided. Ina fourth step S4 at least part of the pose of the second camera atabsolute scale is determined according to the second image and theinformation on the absolute scale of at least part of the second realobject and at least part of the pose of the fourth camera at absolutescale is determined according to the fourth image and the information onthe absolute scale of at least part of the second real object.

In another embodiment, it is also possible to not determine the full twoposes in relation to the second real object (e.g. a human face), that isthe pose of the second camera where the second image is captured and thepose of the fourth camera where the fourth image is captured, but toonly determine the difference between the two poses, in other words justdetermine the pose of the fourth camera with respect to the secondcamera.

In another embodiment, it is also possible to only determine thetranslational distance between the two poses, i.e. just determine thedistance between the pose of the fourth camera and the pose of thesecond camera.

In a next step S5 at least part of the pose of the first camera atabsolute scale is determined according to the pose of the second cameraand the spatial transformation between the first camera and the secondcamera and at least part of the pose of the third camera at absolutescale is determined according to the pose of the fourth camera and thespatial transformation between the third camera and the fourth camera.

In another embodiment, it is also possible to not determine the full twoposes in relation to the first real object, that is the pose of thefirst camera where the first image is captured and the pose of the thirdcamera where the third image is captured, but to only determine thedifference between the two poses, in other words just determine the poseof the third camera with respect to the first camera.

In another embodiment, it is also possible to only determine thetranslational distance between the two poses, i.e. just determine thedistance between the pose of the third camera and the pose of the firstcamera.

This distance can then be used together with the estimated distancebetween the second and the fourth camera to determine an absolute scalefactor for the first camera (e.g. a so-called world-facing camera orback facing camera). For example, the scale factor could define truesizes of reconstructed geometrical models of real environments or beused to map coordinates of a reconstruction at an arbitrary scale toabsolute scale.

In a step S6 spatial coordinates of a 3D reconstruction (also called ageometrical model) of the first real object at absolute scale aredetermined from the at least part of the pose of the first camera andthe at least part of the pose of the third camera and the first imageand the third image, or determined from the difference between the twoposes, or determined from the distance between the two poses.

In another embodiment, the absolute scale factor for the first camera(e.g. world-facing camera) is calculated by the distance between thefirst and third camera (in the first common coordinate system) and thedistance between the second and the fourth camera (in the second commoncoordinate systems).

FIGS. 2a and 2b illustrate a possible embodiment of the presentinvention and the ambiguity in scale when using monocular SLAM or SfM.FIG. 2a shows a top-down view of a scene comprising a large car O1 (e.g.a real car for driving), four cameras W1, F1, W2, and F2, whichcorrespond to the first, second, third, and fourth cameras, and a userU1. It further shows in four insets the images I(W1), I(F1), I(W2), andI(F2) taken by the respective four cameras W1, F1, W2 and F2. FIG. 2bshows a top-down view of a scene comprising a small car O2 (e.g. a toycar for children), four cameras W3, F3, W4 and F4, which correspond tothe first, second, third, and fourth cameras and a user U1. It furthershows in four insets the images I(W3), I(F3), I(W4), and I(F4) taken bythe four cameras W3, F3, W4 and F4.

It is assumed that, even though the large car O1 and the small car O2have a significantly different size, the images I(W1) and I(W3) as wellas images I(W2) and I(W4) are substantially identical. This is becausethe poses of the cameras W3 and W4 are scaled relative to W1 and W2 inthe same way as O2 is scaled relative to O1. This shows the ambiguity inscale. It is impossible to determine the absolute size of the car basedonly on one or more images of it and consequently it is impossible todistinguish the large car O1 from the small car O2 based on the imagesI(W1), I(W2), I(W3), or I(W4). Consequently, it is also not possible todetermine the distance at absolute scale between the camera position ofW1 and W2 or W3 and W4 based on the images if the size of the car isunknown. However, if the absolute distance between the camera positionsof W1 and W2 was known, e.g. to be 1 meter, then it is also possible todetermine the absolute size of the car. There are many applications inwhich it would be beneficial to determine the absolute size ofreal-world objects. Thus, according to the present invention, it isadvantageous to determine the absolute size of real-world objects and toreconstruct them at absolute scale.

In addition to a car, a user U1 (i.e. a human) is located within bothscenes shown in FIGS. 2a and 2b . According to an embodiment of theinvention, we capture images I(F1), I(F2) as well as I(F3), I(F4) of theuser U1 including his or her face. In one embodiment, the spatialtransformations between the camera poses of F1 and W1, F2 and W2, F3 andW3, and between F4 and W4 are known and potentially the same if theimages were captured with two rigidly connected cameras, i.e. W1, W2,W3, and W4 are the same physical camera at different points in time andF1, F2, F3, and F4 are the same physical camera at different points intime. In another embodiment, W1, W2, W3, and W4 are different camerasand F1, F2, F3, and F4 are different cameras.

Because the user U1 does not change his/her size between FIG. 2a andFIG. 2b , the corresponding images I(F1) and I(F2) are different fromimages I(F3) and I(F4), respectively. The position of the face differsmore between I(F1) and I(F2) than between I(F3) and I(F4) which isindicative of that the motion between the cameras W1 and W2 is largerthan that between W3 and W4. We now assume that at least one spatialproperty for at least one facial fiducial of the user's face is given atabsolute scale, e.g. the interpupillary distance is known to be 63millimeters (the eye distance 63 mm is known in a generic face model).And we assume that this fiducial, e.g. the eye center position, can beautomatically localized in the images I(F1) and I(F2) by means of a faceor eye detection algorithm. It is also possible to use other facialpoints, such as one or more of: positions (of corners, centers orbounding areas), size, shape, outlines, regions, scale, ratios anddistances between and the appearance of left and right eyes (pupil,iris, cornea, sclera, inner canthus, outer canthus, center, upper andlower eyelids, eyelashes, . . . ), nasal bridge, nose (Tip, dorsum,alae, nostril, columella, . . . ), philtrum, lips, left and right ears,left and right eye brows, teeth, left and right cheek, jaw, neck,laryngeal prominence. Then the distance between the position of cameraF1 and camera F2 can be determined at absolute scale based on theposition of the facial fiducials in the images I(F1) and I(F2), becausethese fiducials have known spatial properties at absolute scale. Withthe known spatial transformation between camera W1 and F1 as well asbetween W2 and F2, also the distance between the position of camera W1and W2 can be calculated at absolute scale. The same applies for W3, F3,W4 and F4.

The camera images I(W1) and I(W2) together with the known distancebetween the cameras W1 and W2 that took these images finally enables toreconstruct the object O1 at absolute scale or to determine a scalefactor which maps an existing reconstruction of O1 at arbitrary scale toabsolute scale, i.e. to a coordinate system with a known relation toreal-world spatial metrics such as meters or inches.

As a result, the reconstruction of O1 differs from the reconstruction ofO2 in terms of scale, which allows distinguishing between O1 and O2.

FIG. 3 shows another embodiment of the present invention. A scene isillustrated in a top-down view at two different points in time, theupper part of the figure showing the scene at the first point in timeand the lower part of the figure showing the scene at the second pointin time. Insets in the right part of the figure show the images capturedby the respective cameras. A camera W5 is used to capture an image I(W5)of a car O1 and at the same time a camera F5 with a known spatialtransformation to the camera W5 captures an image I(F5) of the user U1and his or her face. Then both cameras are moved away from the cartowards the user resulting in camera poses F6 and W6 and correspondingimages I(F6) and I(W6). Again given only the camera images I(W5) andI(W6) it is not possible to determine the absolute size of the car, i.e.the size of the car in the real world. We assume the intrinsicparameters of the cameras F5, W5, F6, W6, particularly the focallengths, are known. We further assume that the interpupillary distance Pof the user U1 is known in absolute units, e.g. in centimeters, in aspecific face model or a generic face model used for eye detection orface detection. Furthermore the distances (P5 and P6) between the pupilsof the user can be determined in the images I(F5) and I(F6) in pixelcoordinates. In the special case when the image plane of the camera F5has the same distance to both pupils (of the left and right eye), thedistance P5 together with intrinsic camera parameters of F5 aresufficient to compute the distance D5 between the point between thepupils and the camera center of F5 at absolute scale, i.e. in absolutereal-world units, such as meters, as D5=(f*P)/P5 where f is the focallength of camera F5 in pixels (applies if the face is centered aroundthe principal point of the camera). Analogously, when the image plane ofthe camera F6 has the same distance to both pupils (of the left andright eye), the distance D6 between the user U1 and the camera F6 can bedetermined based on the focal length of F6, the pupil distance P6 inimage I(F6) and the interpupillary distance P of the user U1 at absolutescale. If the user U1 does not move in the scene relative to the car O1,then the difference between the distances D5 and D6 can be used todetermine the distance between the position of camera W5 and theposition of camera W6 at absolute scale. This enables determining theabsolute scale of the car O1 under consideration of the camera imagesI(W5) and I(W6).

FIG. 4 illustrates an embodiment of involved coordinate systems andtransformations. The object coordinate system O is defined in absoluteunits with a known scaling factor to real-world distances, such asmeters. The coordinate system W is associated with the world-facingcamera (such as W1, W2, etc.) and the coordinate system F is associatedwith the user-facing camera (such as F1, F2, etc.). When performing SfMor SLAM on images of the world-facing camera, then the coordinate systemW of the camera is defined at an arbitrary scale, as discussed above.The transformation (i.e. rotation R and translation T (at absolutescale)) between W and F is assumed to be known. It may be static andcalibrated once offline (i.e. before use in the scene), for example ifthe two cameras are the user-facing and the world-facing cameras of amobile phone or a tablet computer. The coordinate system F is defined atabsolute scale, because its pose is estimated from facial features inthe coordinate system U of the user's face, which is defined at absolutescale and the facial features are defined at absolute scale as well. Oneof the ideas of this invention is now to take advantage of the fact thatF may be defined at absolute scale and that the rigid bodytransformation between F and W is known. As a result, in the presentembodiment, also W can be defined at absolute scale, which here wouldnot be possible without the coordinate systems F and U, i.e. without auser-facing camera and the user's face.

FIG. 5 shows an embodiment of a capturing apparatus comprising of auser-facing camera and a rigidly attached world-facing camera at twodifferent poses. The world-facing and the user-facing camera can be atleast two separate cameras or a single camera, e.g. an omnidirectionalcamera capturing a 360° field of view. In this case, the camera imagesof the respective cameras may be defined as two different parts of thewhole image. For a single camera, the poses F and W may be equal.

The cameras used at poses F8 and W8 can either be the same cameras asare used at poses F7 and W7 at a different point in time, or they may bedifferent cameras used simultaneously or at different points in time.The images of the user-facing cameras enable determining informationabout the spatial distance (which is a scalar value) at absolute scalebetween the camera positions of F7 and F8 and inferring informationabout the spatial distance d (which again is a scalar value) at absolutescale between the positions of cameras W7 and W8 as well, because thetransformations between F7 and W7 as well as between F8 and W8 areknown.

FIG. 6 shows an exemplary embodiment of the present inventionimplemented with a handheld device H9 such as a mobile phone, smartphone, phablet, or tablet computer comprising a user-facing camera F9which captures the user's face U9 and a world-facing camera W9 whichcaptures a real object O9. Further, the handheld device H9 may have aprocessing device P9 and a wireless network unit. Any step of anyembodiment disclosed herein could be either executed locally in thehandheld device H9 by the processing device P9 (e.g. a commonly usedmicroprocessor) or sent to a remote server computer S9 or another mobiledevice through the wireless network unit. The user facing camera, theworld facing camera and the display screen of the handheld device mayhave known spatial relationships between each other.

According to an embodiment, the processing system as described hereinmay be comprised at least in part in the handheld device H9 and/or inthe server computer S9 adapted to communicate with the handheld deviceH9, e.g. wirelessly. The processing system may be comprised in only oneof these devices, e.g. in the handheld device H9 or in the servercomputer S9, or may be a distributed system in which one or moreprocessing tasks (performing one or more method steps) are distributedand processed by one or more processing devices (such asmicroprocessors) which are spatially distributed and are communicatingwith each other.

With such system setup, the user U9 may reconstruct the real object O9,which is a chair in this example at absolute scale by moving thehandheld device H9 to at least two different viewpoints while not movingthe head U9 relative to the real object O9. This enables determining thepose of W9 at absolute scale when using the reconstruction of O9 as areference for camera pose estimation. This again enables to superimposevirtual objects (e.g. a virtual pillow) on the camera image 191 of theworld-facing camera W9 at absolute scale (see image 192) instead of awrong arbitrary scale (see image 193). This results in more realisticaugmentations that have a consistent scale with the real world in image192, while the virtual pillow is too small relative to the real chair inimage 193.

FIG. 7 illustrates an example of a graphical user interface which guidesa user through a process of scale estimation according to an embodimentof the invention. In this embodiment the method is being performed on amobile phone D2 comprising a display device D1 (which is a touch screenin this case), a user-facing camera F10 and a world-facing camera at therear of the phone which is not visible in the figure. In thisembodiment, the world-facing camera is repeatedly capturing images ofthe real object (car) placed behind the mobile phone D2. These imagesare shown on the display device D1. In the initial state S11 the usercan start the scale estimation process by touching a button G1 on thedisplay. From this point on, the user-facing camera F10 is used todetermine the pose of the user-facing camera relative to the user's faceat absolute scale. Additionally from this point on the world-facingcamera is used to determine the pose of the world-facing camera relativeto the real object (car) at arbitrary scale. Afterwards, in stage S12,the button changes its appearance to indicate that it is disabled (seeempty button G2). Additional graphical user interface elements G3 and G4appear for giving instructions for camera motion to perform and tovisualize the progress of the process. Further, a label G5 may bedisplayed on the display device providing textual instructions orinformation. After the user moved the phone (and thereby theworld-facing camera) in state S13 the progress indicator G6 adaptsaccordingly and shows that progress has been made. Once the cameramotion is sufficiently large for the scale estimation (S14), theprogress indicator G7 visualizes that the target has been achieved.Further, the label G8 may inform the user about the success or failureof the calibration. At his point in time, the user-facing camera as wellas the world-facing camera may potentially stop capturing and/ortracking. Afterwards, the application may be in the initial state S11again, except that potentially the absolute scale of the real object(car) is now estimated and the world-facing camera can determine thepose of the world-facing camera relative to the real object (car) atabsolute scale. The user may trigger another scale estimation procedureby touching the button G1 again.

FIGS. 8a, 8b, and 8c show an influence of the spatial transformationbetween the first and second camera. The first camera and third cameracan be implemented by the same physical camera (e.g. a world-facingcamera, e.g. of a mobile device) and the second camera and the fourthcamera can be implemented by the same physical camera (e.g. auser-facing camera, e.g. of a mobile device). Together, the two cameras(e.g., world-facing and user facing camera) can be seen to form a dualcamera with non-overlapping camera frustum between the two cameras.

Three different set-ups of such dual camera are depicted in theembodiment of FIGS. 8a, 8b and 8c differing in the length of the spatialtranslation between the first camera and the second camera (and therebyalso between the third camera and the fourth camera). Each sub figurecontains a top-down view of a scene comprising the four cameras, witheach sub figure showing the dual camera in two poses with the same posesfor the first camera and the third camera in all sub figures. Theorigins of the cameras are depicted as OR1, OR2, OR3, and OR4. Beloweach top-down view the absolute spatial distance between the cameraorigins OR1 and OR3 (at the top) as well as the absolute spatialdistance between the camera origins OR2 and OR4 (at the bottom) areplotted. The absolute spatial distance between the camera origins isnoteworthy because it contains information about the absolute scale.

The rotational motion between the first camera (index “1”) and the thirdcamera (index “3”) induces a translational motion between the secondcamera (index “2”) and the fourth camera (index “4”) which depends onthe length of the spatial translation between the first camera and thesecond camera and induces a difference in length of the absolute spatialdistance between OR1 and OR3 compared with the length of the absolutespatial distance between OR2 and OR4.

In FIG. 8a , where the length of the spatial translation between thefirst camera and the second camera is zero, the absolute spatialdistance between the camera origins OR1 and OR3 (at the top) as well asthe absolute spatial distance between the camera origins OR2 and OR4 (atthe bottom) are the same.

In FIG. 8b , where the length of the spatial translation between thefirst camera and the second camera is quite small compared to thetranslational motion between the second and the fourth camera, theabsolute spatial distance between the camera origins OR1 and OR3 (at thetop) as well as the absolute spatial distance between the camera originsOR2 and OR4 (at the bottom) are similar but not exactly the same and canbe considered equal when allowing a small error to be introduced.

In FIG. 8c , where the length of the spatial translation between thefirst camera and the second camera is not small compared to thetranslational motion between the second and the fourth camera, theabsolute spatial distance between the camera origins OR1 and OR3 (at thetop) as well as the absolute spatial distance between the camera originsOR2 and OR4 (at the bottom) are quite different and cannot be consideredequal without allowing a large error.

Based on a provided first spatial transformation between the firstcamera and the second camera (cf. step e) described above) at least partof the pose (P1F) of the first camera according to the pose P2F isdetermined (cf. step h)). Also based on a provided second spatialtransformation between the third camera and the fourth camera (cf. stepe)) at least part of the pose (P3F) of the third camera according to thepose P4F is determined (cf. step h)).

The absolute spatial distance between the pose of the first camera andthe pose of the third camera is determined according to the pose of thesecond camera, a spatial transformation T1 between the first camera andthe second camera, the pose of the fourth camera and a spatialtransformation T2 between the third camera and the fourth camera, whereT1 and T2 may be the same This transformation may potentially be a 6Drigid body transformation that may include a calibration of the relativespatial position and orientation between the first camera and the secondcamera in a coordinate system at absolute scale. This could bedetermined using a method such as disclosed in reference [11]. In thiscase the translational motion between camera 2 and camera 4 induced by arotational motion between camera 1 and camera 3 is considered and thecalculation of the absolute spatial distance between the pose of thefirst camera (camera 1) and the pose of third camera (camera 3) isexact.

According to another embodiment (cf. second aspect of the invention asdescribed above), the method treats the spatial distance between cameraF (second camera) and camera W (first camera) as being zero. Thereby thetranslational motion between the pose of the third camera and the firstcamera induced by a rotation between the pose of the second camera andthe pose of the fourth camera is being ignored. The induced error forthe translational part of the calculated poses of the first camera andthe third camera is less or equal to the actual distance between thefirst camera and the second camera. The error induced for the absolutespatial distance between OR2 and OR4 is less or equal to twice theactual distance between the first camera and the second camera. Theerror induced also depends on the amount of rotation between the pose ofthe second camera and the pose of the fourth camera. A rotation of 180°induces the highest error. A nearly translational only motion betweenthe pose of the second camera and the pose of the fourth camera, that iswith only negligible rotational part, does only induce a negligibleerror.

This shows that for camera set-ups with a distance between the firstcamera and the second camera that is small compared to the translationalmotion between the second and the fourth camera, the translational partof the spatial transformation between the first camera and the secondcamera can be considered being an identity transformation. It also showsthat for camera motions that have only negligible rotational parts (forthe first camera as well as for the second camera) compared to thetranslational motion between the second and the fourth camera, thetranslational part of the spatial transformation between the firstcamera and the second camera can be considered being an identitytransformation.

FIG. 9 illustrates another embodiment of the present invention in whichat least part of the poses of the first camera (e.g. world-facingcamera) is used as input to a 3D reconstruction method such that themethod results in a reconstruction of the object O9 at absolute scale.The poses P2F and P4F of the two, e.g., user-facing cameras C2 (secondcamera) and C4 (fourth camera) can be determined relative to thecoordinate system of the user's face U at absolute scale using a facetracking algorithm. Given the spatial transformation T1 between camerasC1 (first camera) and C2 (second camera) and the spatial transformationT2 between cameras C3 (third camera) and C4 (fourth camera) the pose P1Fof the, e.g., world-facing camera C1 and the pose P3F of the, e.g.,world-facing camera C3 can be determined in the coordinate system of theuser's face U at absolute scale by means of concatenation. Thisembodiment then takes advantage of the determined poses of the twoworld-facing cameras C1 and C3 in a common coordinate system and atabsolute scale to create a 3D reconstruction of the real object O9 atabsolute scale by means of establishing correspondences in the cameraimages of cameras C1 and C3 and triangulation of their depths. In thisembodiment, the 3D reconstruction method does not estimate the cameraposes but uses the provided camera poses at absolute scale instead.

In another embodiment, the 3D reconstruction method estimates the 3Dstructure of the scene and the camera poses and the solution space forthe camera poses is constrained to those pairs of poses that have atranslational distance equal to the known translational distance betweenthe two camera poses C1 and C3 as a result of the procedure as explainedabove.

FIG. 10 illustrates another embodiment of the present invention in whicha scaling factor is determined between a first coordinate system whichis defined at arbitrary scale and a second coordinate system which isdefined at absolute scale. In this embodiment, the poses P2F and P4F ofthe two user-facing cameras C2 and C4 can be determined relative to thecoordinate system of the user's face U at absolute scale using a facetracking algorithm. Given the spatial transformation T1 between C1 andC2 and the spatial transformation T2 between C3 and C4 the pose P1F ofthe world-facing camera C1 and the pose P3F of the world-facing cameraC3 can be determined in the coordinate system of the user's face U atabsolute scale by means of concatenation. Based on these, thetranslational distance between the position of the camera C1 accordingto P1F and the position of the camera C3 according to P3F can bedetermined at absolute scale as Dabs. The camera images of the worldfacing cameras C1 and C3 can be used to determine the pose P1W of thecamera C1 in a coordinate system OA related to the real object O10 atarbitrary scale and to determine the pose P3W of the camera C3 in acoordinate system related to the real object O10 at arbitrary scalebased on a 3D reconstruction of the real object O10 at arbitrary scale.Based on these two poses, the translational distance between theposition of the camera C1 according to P1W and the position of thecamera C3 according to P3W can be determined at the arbitrary scale ofthe reconstruction of the real object O10 as D_arb. Finally, afterdetermining the translational distance between the position of thecamera C1 and the position of the camera C3 in the coordinate system ofthe reconstruction of the real object at arbitrary scale as D_arb anddetermining the translational distance between the cameras C1 and C3 atabsolute scale as D_abs according to camera poses determined by facetracking at absolute scale, it is possible to determine a scale factorbetween the arbitrary scale of the coordinate system of thereconstruction of the real object and absolute scale as D_abs/D_arb.This scale factor can be used to scale the coordinates of the 3Dreconstruction of the real object O10 such that they are defined atabsolute scale after scaling.

In another embodiment, the spatial transformations T1 and T2 are assumedto have a very small translational part which is handled as being zeroand, therefore, instead of computing D_abs between the poses of theworld-facing cameras C1 and C3 relative to the user's coordinate systemat absolute scale, the translational distance D_abs is computed betweenthe two user-facing cameras C2 and C4 relative to the user's coordinatesystem at absolute scale.

FIG. 11 illustrates another embodiment of the present invention andshows how input data may be combined from more than 4 images into afinal estimated scale factor according to an embodiment of the presentinvention.

Box 1101 corresponds to one individual estimation of a scale factorbased on four provided images I(C1), I(C2), I(C3), I(C4), and twoprovided transformations T1, and T2 according to an embodiment of thepresent invention.

Image I(C1) is the image captured by C1, the first camera, showing apart of the first real object. Image I(C2) is the image captured by C2,the second camera, showing a part of the second real object. T1 is theknown transformation between the pose of camera C1 and camera C2. Basedon the image I(C1) showing part of the first real object, the pose P1Wof camera C1 in the first common coordinate system at arbitrary scale isdetermined. Based on the image I(C2) showing part of the second realobject, the pose P2F of camera C2 in the second common coordinate systemat absolute scale is determined. The pose P1F of camera C1 in the secondcommon coordinate system is determined by a transformation of pose P2Fusing the provided transformation T1. This transformation is optional,meaning the translational part of T1 can be considered being an identitytransformation (i.e. the translational part is zero or could beneglected) leading to the translational part of P1F being equal to thetranslational part of P2F.

Image I(C3) is the image captured by C3, the third camera, showing apart of the first real object. Image I(C4) is the image captured by C4,the fourth camera, showing a part of the second real object. T2 is theknown transformation between the pose of camera C3 and camera C4. Basedon the image I(C3) showing part of the first real object, the pose P3Wof camera C3 in the first common coordinate system at arbitrary scale isdetermined. Based on the image I(C4) showing part of the second realobject, the pose P4F of camera C4 in the second common coordinate systemat absolute scale is determined. The pose P3F of camera C3 in the secondcommon coordinate system is determined by a transformation of pose P4Fusing the provided transformation T2. This transformation is optional,meaning the translational part of T2 can also be considered being anidentity transformation (i.e. the translational part is zero or could beneglected) leading to the translational part of P3F being equal to thetranslational part of P4F.

Based on the translational part of P1W, which is the pose of the firstcamera C1 in the first common coordinate system, the translational partof P1F, which is the pose of the first camera C1 in the second commoncoordinate system, the translational part of P3W, which is the pose ofthe third camera C3 in the first common coordinate system, and thetranslational part of P3F, which is the pose of the third camera C3 inthe second common coordinate system, the scaling factor S1, which scalesthe first common coordinate system from arbitrary scale to absolutescale, can be determined.

Box 1102 corresponds to another individual estimation of a scale factorbased on four provided images I(C5), I(C6), I(C7), I(C8), and twoprovided transformations T3, and T4 according to an embodiment of thepresent invention. Thereby images may be different images or partiallythe same images as used in Box 1101. For example I(C5) and I(C6) couldpotentially be the same as I(C3) and I(C4) respectively. In anotherexample, I(C7) and I(C8) could potentially be the same as I(C3) andI(C4) respectively.

Image I(C5) is the image captured by camera C5, showing a part of thefirst real object. Image I(C6) is the image captured by camera C6,showing a part of the second real object. T3 is the known transformationbetween the pose of camera C5 and camera C6. Based on the image I(C5)showing part of the first real object, the pose P5W of camera C5 in thefirst common coordinate system at arbitrary scale is determined. Basedon the image I(C6) showing part of the second real object, the pose P6Fof camera C6 in the second common coordinate system at absolute scale isdetermined. The pose P5F of camera C5 in the second common coordinatesystem is determined by a transformation of pose P6F using the providedtransformation T3. This transformation is optional, meaning thetranslational part of T3 can also be considered being an identitytransformation leading to the translational part of P5F being equal tothe translational part of P6F.

Image I(C7) is the image captured by camera C3, showing a part of thefirst real object. Image I(C8) is the image captured by camera C8,showing a part of the second real object. T4 is the known transformationbetween the pose of camera C7 and camera C8. Based on the image I(C7)showing part of the first real object, the pose P7W of camera C7 in thefirst common coordinate system at arbitrary scale is determined. Basedon the image I(C8) showing part of the second real object, the pose P8Fof camera C8 in the second common coordinate system at absolute scale isdetermined. The pose P7F of camera C7 in the second common coordinatesystem is determined by a transformation of pose P8F using the providedtransformation T4. This transformation is optional, meaning thetranslational part of T4 can also be considered being an identitytransformation leading to the translational part of P7F being equal tothe translational part of P8F.

Based on poses P5W, which is the pose of the camera C5 in the firstcommon coordinate system, P5F, which is the pose of camera C5 in thesecond common coordinate system, P7W, which is the pose of camera C7 inthe first common coordinate system, and P7F, which is the pose of cameraC7 in the second common coordinate system, the scaling factor S2, whichscales the first common coordinate system from arbitrary scale toabsolute scale, can be determined.

Finally the individual scale estimations S1 and S2 may be combined intoa final scale estimate S by means of fitting a mathematical model usingfor example one or more methods like average, mean, median, probabilitymaximization or RANSAC.

The above example contains two individual scale estimations S1 and S2and combines them into a final scale estimate. Of course it isanalogously possible to perform more than two individual estimations,e.g. 3, 4, 5, or 100, etc., and combine all of them.

FIG. 12 illustrates another embodiment of the present invention andshows how input data may be combined from more than 4 images into afinal estimated scale factor according to an embodiment of the presentinvention.

This example is based on six provided images I(C1), I(C2), I(C3), I(C4),I(C5), I(C6) and three provided transformations T1, T2, and T3 accordingto an embodiment of the present invention.

Image I(C1) is the image captured by C1, the first camera, showing apart of the first real object. Image I(C2) is the image captured by C2,the second camera, showing a part of the second real object. T1 is theknown transformation between the pose of camera C1 and camera C2. Basedon the image I(C1) showing part of the first real object, the pose P1Wof camera C1 in the first common coordinate system at arbitrary scale isdetermined. Based on the image I(C2) showing part of the second realobject, the pose P2F of camera C2 in the second common coordinate systemat absolute scale is determined. The pose P1F of camera C1 in the secondcommon coordinate system is determined by a transformation of pose P2Fusing the provided transformation T1. This transformation is optional,meaning the translational part of T1 can also be considered being anidentity transformation leading to the translational part of P1F beingequal to the translational part of P2F.

Image I(C3) is the image captured by C3, the third camera, showing apart of the first real object. Image I(C4) is the image captured by C4,the fourth camera, showing a part of the second real object. T2 is theknown transformation between the pose of camera C3 and camera C4. Basedon the image I(C3) showing part of the first real object, the pose P3Wof camera C3 in the first common coordinate system at arbitrary scale isdetermined. Based on the image I(C4) showing part of the second realobject, the pose P4F of camera C4 in the second common coordinate systemat absolute scale is determined. The pose P3F of camera C3 in the secondcommon coordinate system is determined by a transformation of pose P4Fusing the provided transformation T2. This transformation is optional,meaning the translational part of T2 can also be considered being anidentity transformation leading to the translational part of P3F beingequal to the translational part of P4F.

Image I(C5) is the image captured by a camera C5, showing a part of thefirst real object. Image I(C6) is the image captured by a camera C6,showing a part of the second real object. T3 is the known transformationbetween the pose of camera C5 and camera C6. Based on the image I(C5)showing part of the first real object, the pose P5W of camera C5 in thefirst common coordinate system at arbitrary scale is determined. Basedon the image I(C6) showing part of the second object, the pose P6F ofcamera C6 in the second common coordinate system at absolute scale isdetermined. The pose P5F of camera C5 in the second common coordinatesystem is determined by a transformation of pose P6F using the providedtransformation T3. This transformation is optional, meaning thetranslational part of T3 can also be considered being an identitytransformation leading to the translational part of P5F being equal tothe translational part of P6F.

Based on poses P1W, P3W, and P5W, i.e. the poses of the cameras C1, C3,and C5 in the first common coordinate system, and poses P1F, P3F, andP5F, i.e. the poses of the cameras C1, C3, and C5 in the second commoncoordinate system, the scaling factor S, which scales the first commoncoordinate system from arbitrary scale to absolute scale, can bedetermined by means of fitting a mathematical model using for exampleone or more methods like iterative closest point (ICP), the Umeyamamethod, or Kabsch-method or other methods of least-squares and/or RANSACmodel fitting and point based registration.

The FIG. 12 illustrates using 3 cameras capturing the first object and 3cameras capturing the second object. Of course it is also possible tomake use of even more than 3 poses of cameras in the first commoncoordinate system and corresponding 3 poses of respective cameras in thesecond common coordinate system, e.g. 4, 5, 6, or 100, etc. pairs ofposes in the first and second coordinate system.

In the following, further embodiments of the invention are disclosedwithout expressly referring to the drawings or figures.

According to a further embodiment, the method assumes that the user'sface is positioned statically with respect to the real object to betracked or reconstructed while capturing images contributing to thescale estimation.

According to a further embodiment, the method detects when the user'sface is positioned statically with respect to the real object, and thescale estimation then only uses images which were captured during thetime when the user's face is positioned statically with respect to thereal object. This can for example be done by comparing the epipolargeometries of two poses of the user-facing camera with the featuremovement in the world-facing camera or vice-versa. Another approach todetermine whether the head moved with respect to the first real objector not is based on the set of corresponding poses of the user- and theworld-facing camera. These poses can be interpreted as 3D points beingindicative of the camera position. An algorithm to determine thesimilarity transformation between two sets of points is for exampledisclosed by Umeyama [10]. After registration the residual error can becomputed. If the residual error is above a particular threshold, the twosets of corresponding poses are considered to not be related by a rigidbody transformation. This indicates that the head (i.e. face) has beenmoved relative to the first real object.

According to a further embodiment, the method deals with and compensatesfor the motion of the face relative to the first real object byestimating the head motion relative to the real object using visualtracking to estimate the motion of the face in the camera image of theuser-facing camera and the motion of first real object in the cameraimage of the word-facing camera separately.

According to a further embodiment, the method deals with and compensatesfor the motion of the face relative to the real object by estimating thehead motion relative to the real object using visual tracking toestimate the motion of the face and the motion of the background in thecamera image of the user-facing camera separately.

According to a further embodiment, the calculation of the absolutespatial distance between cameras W1 and W2 from poses of cameras F1 andF2 may include a calibration of the relative spatial position andorientation between coordinate systems F and W in a coordinate system atabsolute scale, e.g. using a method such as disclosed in reference [11].

According to another embodiment the transformation between coordinatesystems F and W is a 6 DoF (DoF: Degrees of Freedom) rigid bodytransformation comprising a 3D rotation and a 3D translation.

According to a further embodiment the method is provided with andconsiders the spatial distance between coordinate systems F and W.

According to a further embodiment, the method treats the spatialdistance between coordinate systems F and W as zero, thereby ignoringthe translational movement for the pose of coordinate system (camera) Finduced by a rotation of coordinate system (camera) W and vice versa,which leads to an error for poses of cameras F1 and F2 corresponding toposes of cameras W1 and W2 that is less or equal to the actual distancebetween coordinate system (camera) F and (camera) W.

According to a further embodiment, the method treats the difference inorientation between coordinate systems F and W as 180 degrees, i.e. thatthe optical axis of the respective cameras are parallel and the camerasare facing in opposite directions.

According to an embodiment the method uses a facial fiducial trackerlike disclosed in reference [15] to do face alignment and determine the2D positions of the facial features and fiducials in the image.

According to an embodiment the method uses a face tracking methoddelivering the full 6D pose of the user-facing camera in relation to theface like disclosed in reference [16] where a statistical anthropometric3D rigid model is used as an approximation of the human head. Thedelivered 6D pose then afterwards is potentially modified in scale andtranslation by additional specifications for the dimensions ofparticular facial features, like eye distance.

According to an embodiment the method uses a face tracking methoddelivering the full 6D pose of the user-facing camera in relation to theface at absolute scale based on a previously acquired, fitted orconfigured model of the particular face of the user.

According to an embodiment the method uses a face tracking methoddelivering a 3D pose containing the orientation of the face in relationto the user-facing camera like disclosed in reference [17], and usingthis 3D pose together with detected facial features, to infer additionaltranslational information about the pose. The viewing angle between thetwo locations corresponding to facial features can for example be usedtogether with the expected real-world distance between these twolocations compensated for the rotation of the head to solve for thedistance of the face from the camera. The change in distance betweendifferent poses can be used to infer information about the absolutescale of the camera movement.

According to an embodiment the method uses a face tracking methoddelivering at least a 1D pose containing the left-right orientation(i.e. yaw orientation) of the face in relation to the user-facing cameralike disclosed in reference [17], and using at least this 1D posetogether with detected facial features that are assumed to bedistributed on a horizontal line on the face like the eye centers, toinfer additional translational information about the pose. The viewingangle between the two eye centers can be used together with the expectedreal-world eye distance compensated for the 1D left-right rotation (yaw)of the head to solve for the distance of the face/eyes from the camera.The change in distance between different poses can be used to inferinformation about the absolute scale of the camera movement.

According to another embodiment the method assumes that poses of camerasF1 and F2 are restricted to being frontal to the face, with pose of F2differing from pose of F1 in a (approximately) pure translation towardsthe face or away from the face, using the detected positions of the eyesdetected in the captured images by, for example, a method as disclosedby reference [18] and the associated viewing angle between the eyestogether with the expected real-world eye distance to solve for thedistance of the face/eyes from the camera. This is also illustrated inFIG. 3. The viewing angle between the two eyes can be calculated by thedot product of the respective viewing directions delivering the cosineof the viewing angle. The distance to the face then can be calculated byhalf the eye distance divided by the tangent of the half viewing angle.

The change in distance between the corresponding poses of cameras F1 andF2 and W1 and W2, respectively, can be used to infer information aboutthe absolute scale of the coordinate systems of cameras W1 and W2.

According to an embodiment, at least for one facial feature a spatialproperty, for example the human interpupillary distance, is provided inabsolute spatial units, whereby the property can be given as a singlevalue or as a probability distribution. The property can be eitherspecified individually for a particular user, or be generic for aplurality of people. Also multiple values/probability distributions canbe defined for different groups of people (gender, ethnic groups, age, .. . ) and the relevant group can be selected either by a manual userinput or another automatic labelling or classification procedure as forexample disclosed in reference [12].

According to an embodiment the particular face is calibratedautomatically based on information about absolute scale provided for thesecond camera (e.g. user-facing camera). The information about absolutescale can for example be provided by depth information about the secondreal object by depth from defocus, time of flight, structured light,active lighting methods, luminance based methods, photo light, laserrangefinder, multiple frequency phase-shift, interferometry or passivestereo. Stereo methods with a small baseline as is the case for mobiledevices may work more reliably on the second camera (e.g. user-facingcamera) with the second real object being a face which is close to thecameras, usually closer than 50 cm, in contrast to stereo methods on thefirst camera (e.g. world-facing camera) with a first real object beinglocated arbitrarily further away than the face. This is because the sizeof the required baseline for a certain depths “resolution” at the depthsof the captured object depends on the distance of the captured object tothe camera.

According to an embodiment the particular face is calibrated manually ora generic model is used. Thereby a statistical model also allows todetermine uncertainty in scale estimation given how different spatialproperties of the face vary among different humans.

The manual calibration of the interpupillary distance can for example bedone using a mirror and a ruler. Facing the mirror and keeping the headupright and frontal to the mirror, the ruler is placed horizontally infront of the face, as close as possible and below the eyes, with themeasurement markers visible in the mirror. The following measurementsshould be performed without moving the head or the ruler. Closing oneeye, the other open eye can read the measurement at the ruler below thecenter of the pupil. This process can be repeated with the other eye(close the previously open eye and open the closed one). The differencebetween the two readings gives the interpupillary distance. Asemiautomatic calibration at absolute scale of the interpupillarydistance or other facial features can for example be performed using adual camera set-up, performing camera pose estimation at absolute scaleusing the images captured by the back facing camera (for example markerbased tracking, object tracking or SLAM at absolute scale). At the sametime, the facial features to be calibrated are tracked on theuser-facing camera. With the user's face being positioned staticallywith respect to the real-world object used for the tracking on the backfacing camera, the absolute scale can be transferred to the facialfeatures by applying the known transformation between the camera poses.

A generic model can for example contain the mean value and theprobability distribution for absolute spatial properties of facialfeatures, such as the interpupillary distance, based on statistics asdisclosed in reference [13]. Such kind of model may also includeinformation about the uncertainty of a certain value. A generic modelcan also comprise a multitude of (joint) probability distributions formultiple measurements so that the absolute scale is determined accordingto the location in parameter space of the maximum probability over thejoint probability distributions according to properties of the observedfacial features.

According to an embodiment, the method combines absolute scale estimatesof different sources (including at least one absolute scale estimationbased on at least one image of the user's face), and any of IMU, GPS,known objects in the scene, depths from defocus, manual input, passivestereo, time of flight, structured light, active lighting methods,luminance based methods, projective foreshortening based depthestimation, and the history of scale estimates of previous objectreconstructions potentially combined with object classification to onlyconsider previous object reconstructions of previous objects.

According to an embodiment, the scale estimation can be performed as aniterative process using multiple pose pairs each comprising two poses attwo points in time. Inconsistencies between different measurements canbe detected and the best consensus for the scale can be determined bycombining the different measurements by means of for example taking theaverage, median, histogram based maximum, potentially weighted based onfor example uncertainties, age or intra measurement inconsistencies ofthe individual scale estimations. The combination of the differentmeasurements can also be performed by a model fitting method, such as animplementation of a Bayes filter like a Kalman filter to infer theabsolute scale.

Also for many use cases a limited variation in distance between theuser's face and the user-facing camera can be assumed, for example withthe user being close to the device because he is holding the device orbecause the user is next to the display device to experience an ARapplication and thereby also near the user-facing camera. This limitedvariance in distance makes the scenario more robust for scale estimationwith standard cameras. It also allows for applying user-facing depthcameras that only estimate depth in short range and cannot estimate thedepth of far away objects, such as the next house.

A possible embodiment of the invention comprises a combination with auser-facing depth camera, which enables scale estimation from theappearance of the face of the user to rely on more accurate informationabout the actual face model geometry and absolute spatial dimensions.This allows extracting more details and elevates the need to either relyon statistics from basic standard face models or to fit and warp somegeneric face model or to configure user specific scale values. Anotherbenefit of using a depth camera is that face detection and poseestimation can be performed within very dark or very bright environmentsor in environments in which the illumination changes strongly and/orfrequently. In such environments standard monocular low dynamic rangecameras will most probably fail to detect the face and estimate the poseof the face.

Another possible embodiment of the invention comprises a second cameraand/or fourth camera being a depth camera (e.g. a user-facing depthcamera), which enables scale estimation from any real object present inthe frustum of the depth camera, using visual odometry based on depthinformation. This results in camera poses at absolute scale even if nofacial properties are being taken advantage of.

In another embodiment, the second camera and/or fourth camera (e.g.user-facing camera) is an infrared camera, which is particularly wellsuited to detect and track faces, or it images in at least one of thefollowing bands: extreme ultraviolet, near ultraviolet, near infrared,mid infrared, long-wavelength infrared, or far infrared.

In another embodiment the second camera and/or fourth camera (e.g.user-facing camera) is comprised of multiple cameras, such as a passivestereo camera or any other combination of two or more cameras,potentially imaging different bands of the light spectrum such asvisible light, extreme ultraviolet, near ultraviolet, near Infrared, midinfrared, long-wavelength infrared, or far infrared.

According to an embodiment, the method may further comprise detecting afirst user input.

According to an embodiment, the user is required to perform a certainmotion with the second camera (e.g. user-facing camera), e.g. a 30 cmtranslation, which is measured based on images captured with the secondcamera (e.g. user-facing camera). An application may visualize theprogress in doing so as illustrated in FIG. 7.

A user interaction may trigger the process of absolute scale estimation.A user interaction may be pressing a button, touching a screen, speechrecognition and/or gesture recognition.

In another embodiment, the method is performed without any interactionwith the user interface. In this embodiment, the method is performed inthe background and determines a scale estimate when the camera motion issuited for doing so.

Moreover, the invention is concerned with a computer implemented userinteraction method, as described herein, for a user to interact with amethod according to the present invention.

A user input may include one or more user interactions. A userinteraction could be one of speaking, running, jumping, blinking, and/ormoving any parts of the user. A user interaction may also be pressing abutton, touching a screen, speaking into a microphone, gazing orgesturing. The user interaction may also be placing a real object into afield of view of a camera such that the real object is recognized basedon an image of at least part of the real object captured by the camera.

The user input then also may be the particular camera motion performedpotentially while holding a button. Such particular motion may be movingthe camera close to the face and away from the face, or moving thecamera up and down, or moving the camera left and right.

A mobile device, as may be used herein, contains at least one cameraused to capture images. The mobile device further has a processingdevice that can be used to perform any of the steps according to theinvention as described herein. The mobile device also includes atouchscreen that can display a graphical user interface such that a usercan touch or press physical or displayed buttons of the graphical userinterface.

Embodiments of the invention are described herein with reference tousing a mobile or a handheld device, such as a mobile phone, but theinvention may be applied in principle with any processing device, suchas implemented in commonly available computer devices comprising one ormore microprocessors, for performing the respective steps (such as awearable computer, tablet computer, mobile computer, often calledlaptop, or a head mounted display, such as used for optical see-throughaugmented reality applications). The steps as described herein may alsobe performed by a network of processing devices, such as a network ofcomputers and/or a mobile device communicated with a server computer.

Embodiments of the invention could be employed in a variety ofapplications including augmented reality applications that enableplacing and displaying virtual objects in a real environment, navigationapplications that use the camera to estimate the position and/or themotion of a user, simulations how captured objects would spatiallyrelate to other objects or environments, or measurement applicationsthat aim at measuring distances between points on a reconstructed objectat absolute scale.

For example an embodiment of the present invention could be employed inan augmented reality application. A reconstruction of a real object atabsolute scale is used as basis to determine the pose of a camerarelative to the object based on establishing 2D-3D correspondencesbetween camera images and the reconstructed model of the real object.Such Augmented Reality applications may superimpose virtual 3D objects,such as a virtual chair, on a live view such that they appear to bestatic relative to the real object. This requires knowledge on thecamera pose (i.e. position and orientation) relative to the real object.Since the reconstruction of the real object is defined at absolute scalethanks to an embodiment of this invention, also the pose can beestimated at absolute scale, which enables superimposing the virtualobject at absolute scale. Thereby, if the real object is a table and thevirtual object is a chair, then the virtual chair would appear at aconsistent size with the real table when placed next to it. In contrast,when using a reconstruction at arbitrary scale, the chair might be 5times as high as the table or only have a fifth of the height of thetable, which both results in unrealistic appearance. Having areconstruction of a real object or environment at absolute scale isparticularly crucial in Augmented Reality applications that shouldprovide the user with the opportunity to assess whether certain objectwould spatially fit into the real environment by superimposing a virtualmodel of it. For example, such application could be used to visuallyassess, whether a sofa fits in a living room or if it is too large.Without knowing the absolute scale as provided by this invention, thiswould not be possible.

In another example an embodiment of the present invention could be usedin a measurement application. A reconstruction of a real object isperformed at absolute scale using an embodiment of the presentinvention. A software application might then provide a user with theoption to select points on the reconstruction, e.g. by means of pointingand clicking on them with a pointing device, such as a mouse. Theapplication may then compute the (Euclidean) distance between twoselected point and display the distance to the user. If such applicationoperates on a reconstruction at absolute scale, as provided by thisinvention, then the computed distances are at absolute scale, e.g. inmillimeters, as well. Contrary, when using a reconstruction at arbitraryscale, measured distances on the reconstruction could only be used tocompare distances on the object relative to each other, but allmeasurements would not relate to absolute units, such as millimeters orinches.

In another example an embodiment of the present invention could beemployed in vision-based navigation, which is used to estimate themotion of a user based on visual odometry, to localize the user as wellas to provide feedback. It needs to estimate the egomotion at absolutescale in order to update the position relative to the coordinate system,where the map and navigation information is stored in. Without absolutescale only the shape of the covered trajectory is determined and noinformation about the real distances covered in the real world. Also theshape can be severely distorted over time as a result of drift in scale.Indoor navigation thereby requires long-term robustness and consistencyfor the measurements. Also velocity can be of interest, which directlydepends on the scale of the reconstruction, since it refers to distancedivided by time. Without a reconstruction at absolute scale, also thevelocity information gained from motion estimation is not at absolutescale.

Even other applications benefit from the very fact, that the scale of areconstruction is repeatable even if the absolute relation to real-worlddistances is unknown. This repeatability is accomplished by thepresented method. When for example creating reconstructions for severalparts of a scene individually it is desirable that the individual mapsare defined at the same scale. This makes combining the individual partsof a scene easier. A repeatable scale also allows overcoming scale driftthat can happen during longer reconstruction runs.

Generally, in the following, a further explanation of terms is given andthe following further aspects and embodiments may be applied inconnection with aspects of the invention.

A camera as used herein is an image capturing device to capture imageryinformation of real objects. Intrinsic camera parameters of the at leastone camera or cameras may be provided or calibrated. The presentinvention can be applied with receiving image information from anycamera providing images of real objects. It is not restricted to camerasproviding color images in the RGB format. It can also be applied to anyother color format and also to monochrome images, for example to camerasproviding images in grayscale format. The used camera or cameras mayfurther provide an image with depth data. The depth data does not needto be provided in the same resolution as the (color/grayscale) image. Acamera providing an image with depth data is often called RGB-D camera.A RGB-D camera system could be a time of flight (TOF) camera system or acamera system using structured light. The at least one camera or camerascould also capture light that is invisible to human eyes. For example,the at least one camera or cameras may be an infrared camera capturinginfrared lights.

A real object may be any object that has a physical geometric shape inthe real world. The real object could further include one or more realobjects. For example, the real object may be an interior of a room or avehicle. The real object could also be, for example, a chair, abuilding, a mountain, a tree or a table. An interior of a room (as areal object) may further include a chair and/or a table. A real objectis also referred to herein as real environment or real scene. The realobject can also be an arrangement of a plurality of real objects. Forexample, an indoor object may be an arrangement of a chair, a table, anda sofa.

A geometrical model (or 3D map or also called 3D reconstruction) of thereal environment can be created using triangulation of 2D observationsshared in a plurality of images captured by one or more cameras.Triangulation is a common method used in 3D reconstruction procedure,based on camera images, also called Structure from Motion (SfM), seereference [2].

A pose of a camera describes a spatial relationship or a rigidtransformation between the camera at a particular position and areference coordinate system. The reference coordinate system may beassociated with a real object or with the camera at another position.The spatial relationship or the rigid body transformation describes atleast one translation, or at least one rotation, or their combination in3D space or at least one distance.

3D features represent or describe physical 3D features of the respectivereal object or at least part of the respective real object. The 3Dfeatures are, for example, but not limited to, points, edges, lines,segments, corners and/or any other geometrical shapes.

To describe a geometry of objects, positions of points and othergeometric elements and other properties thereof like for exampledirections are uniquely determined in a coordinate system. A particularelement, e.g. a position, is defined by an ordered tuple of numbers, thecoordinates.

We in the following refer to a Cartesian coordinate system, bearing inmind that representations of geometrical figures in other coordinatesystems (like e.g. polar coordinates or homogeneous coordinates) can betransformed into the Cartesian coordinate system and vice versa bycoordinate transformations which describe the relation between twocoordinate systems and provide formulas to express particularcoordinates in one coordinate system by corresponding coordinates in theother coordinate system.

In the Cartesian coordinate system, each element of the coordinate tuplecorresponds to the signed distance of the point to the correspondinghyperplane. The distance between two coordinate tuples itself can bedefined as the Euclidean distance describing the length of the linesegment connecting the two coordinate tuples. The distance itselfthereby is also given as a 1D-coordinate.

It should be defined what a unit along a coordinate axis means. Thisunit of measurement is a quantity, used as a factor to express occurringquantities of that property, for example length. We characterizemeasurements (e.g. spatial coordinates defining positions of themeasurements, and respectively coordinates defining spatial distancesand length between the spatial coordinates) to be specified in absolutespatial units, also referred to as at absolute spatial scale, when therelation between a unit of the coordinate system where the measurementsare defined in to a real-world spatial reference unit is known. Thereal-world spatial reference unit can for example be a meter (metricsystem) or any other unit for which a fix and known conversion to metersexists.

Measurements can be at absolute scale by either setting the spatial baseunit of the coordinate system where the measurements are defined indirectly to a real-world spatial reference unit or by specifying thespatial scaling of the coordinate system in relation to a real-worldspatial reference unit.

An example for measurements which are not in absolute spatial units, butin an arbitrarily scaled coordinate system, is for example given by areconstruction of 3D points by triangulation of corresponding pointfeatures from two images captured at different camera poses in space,where the length of the baseline between these two camera poses isunknown. Even though the coordinate axes have the same unit, the unititself is not determined. That means although the ratio between twodistances within the coordinate system is correct, the absolute scalingof the whole coordinate system in relation to the real world is unknown.The coordinate system and thereby the reconstruction itself then is saidto be up to scale. Absolute scale does not refer to the absolutetranslational offset of an object, which depends on the origin of thecoordinate system, but refers to the absolute size.

Facial features and fiducials may comprise positions (of corners,centers or bounding areas), size, shape, outlines, regions, scale,ratios and distances between and the appearance of left and right eyes(pupil, iris, cornea, sclera, inner canthus, outer canthus, center,upper and lower eyelids, eyelashes, . . . ), nasal bridge, nose (Tip,dorsum, alae, nostril, columella, . . . )(size, shape), philtrum, lips,left and right ears, left and right eye brows, teeth, left and rightcheek, jaw, neck, laryngeal prominence, the structure and consistence ofthe skin (like pores), facial and head hair, etc.

Visual odometry refers to methods that determine the position andorientation of a camera by analyzing the associated camera images. Whenprovided with depth information at absolute scale associated to at leastone pixel in one camera image (e.g. the distance of the imaged surfacefor one pixel in millimeters), then visual odometry can determine cameraposes (i.e. position and orientation) at absolute spatial scale. Theterms visual odometry and SLAM are often used interchangeably.

REFERENCES

-   1. Davison, Andrew J., et al. “MonoSLAM: Real-time single camera    SLAM.” Pattern Analysis and Machine Intelligence, IEEE Transactions    on 29.6 (2007): 1052-1067.-   2. Hartley, Richard, and Andrew Zisserman. Multiple view geometry in    computer vision. Vol. 2. Cambridge, 2000.-   3. Azuma, Ronald, et al. “Recent advances in augmented reality.”    Computer Graphics and Applications, IEEE 21.6 (2001): 34-47.-   4. Strasdat, Hauke, J. M. M. Montiel, and Andrew J. Davison. “Scale    drift-aware large scale monocular SLAM.” Proceedings of Robotics:    Science and Systems (RSS). Vol. 2. No. 3. 2010.-   5. Lemaire, Thomas, et al. “Vision-based slam: Stereo and monocular    approaches.” International Journal of Computer Vision 74.3 (2007):    343-364.-   6. Lieberknecht, Sebastian, et al. “RGB-D camera-based parallel    tracking and meshing.” Mixed and Augmented Reality (ISMAR), 2011    10th IEEE International Symposium on. IEEE, 2011.-   7. Klein, Georg, and David Murray. “Parallel tracking and mapping    for small AR workspaces.” Mixed and Augmented Reality, 2007.    ISMAR 2007. 6th IEEE and ACM International Symposium on. IEEE, 2007.-   8. Castle, Robert, Georg Klein, and David W. Murray. “Video-rate    localization in multiple maps for wearable augmented reality.”    Wearable Computers, 2008. ISWC 2008. 12th IEEE International    Symposium on. IEEE, 2008.-   9. Nützi, Gabriel, et al. “Fusion of IMU and vision for absolute    scale estimation in monocular SLAM.” Journal of intelligent &    robotic systems 61.1-4 (2011): 287-299.-   10. Umeyama, Shinji. “Least-squares estimation of transformation    parameters between two point patterns.” Pattern Analysis and Machine    Intelligence, IEEE Transactions on 13.4 (1991): 376-380.-   11. Esquivel, Sandro, Felix Woelk, and Reinhard Koch. “Calibration    of a multi-camera rig from non-overlapping views.” Pattern    Recognition. Springer Berlin Heidelberg, 2007. 82-91.-   12. Han, Hu, et al. “Demographic Estimation from Face Images: Human    vs. Machine Performance.”-   13. Dodgson, Neil A. “Variation and extrema of human interpupillary    distance.”Proceedings of SPIE. Vol. 5291. 2004.-   14. Clipp, Brian, et al. “Robust 6dof motion estimation for    non-overlapping, multi-camera systems.” Applications of Computer    Vision, 2008. WACV 2008. IEEE Workshop on. IEEE, 2008-   15. Ren, Shaoqing, et al. “Face Alignment at 3000 FPS via Regressing    Local Binary Features.”-   16. Martins, Pedro, and Jorge Batista. “Accurate single view    model-based head pose estimation.” Automatic Face & Gesture    Recognition, 2008. FG'08. 8th IEEE International Conference on.    IEEE, 2008.-   17. Asthana, Akshay, et al. “Incremental Face Alignment in the    Wild.” Computer Vision and Pattern Recognition (CVPR), 2014 IEEE    Conference on. IEEE, 2014.-   18. Wang, Peng, et al. “Automatic eye detection and its validation.”    Computer Vision and Pattern Recognition-Workshops, 2005. CVPR    Workshops. IEEE Computer Society Conference on. IEEE, 2005.-   19. Turk, Matthew A., and Alex P. Pentland. “Face recognition using    eigenfaces.” Computer Vision and Pattern Recognition, 1991.    Proceedings CVPR'91, IEEE Computer Society Conference on. IEEE,    1991.-   20. Daniel Kurz, Selim Behimane “Method of providing a descriptor    for at least one feature of an image and method of matching    features” US 20120219188 A1.

The invention claimed is:
 1. A method of determining spatial coordinatesof a 3D reconstruction of at least part of a first real objectcomprising: obtaining, from a first camera system, a first imagecomprising at least part of a first real object; obtaining, from asecond camera system, a second image comprising at least part of asecond real object associated with known geometric properties, whereinthe first camera system and the second camera system have a knownspatial relationship; determining a scale of at least part of the secondreal object based on the second image and the known geometric propertiesof the at least part of the second real object; determining a pose ofthe second camera system based on the determined scale of the at leastpart of the second real object; determining a pose of the first camerasystem according to the pose of the second camera system and the knownspatial relationship; and determining, based on the pose of the firstcamera system and the scale of the at least part of the second realobject, spatial coordinates of a 3D reconstruction of the first realobject.
 2. The method of claim 1, wherein the second real objectcomprises at least part of a human face.
 3. The method of claim 2,wherein the scale of at least part of the human face is determined basedon a generic face model.
 4. The method of claim 2, wherein the humanface belongs to a particular user, and wherein the scale of the at leastpart of the human face is determined based on a user-specific facemodel.
 5. The method of claim 1, wherein the first camera system and thesecond camera system are comprised in an electronic device.
 6. Themethod of claim 1, wherein a frustum of the first camera system and afrustum of the second camera system do not overlap.
 7. The method ofclaim 1, further comprising: superimposing a virtual object at a scaleconsistent with the first real object according to the determinedspatial coordinates of the 3D reconstruction of the first real object.8. A non-transitory computer readable medium comprising computerreadable code for determining spatial coordinates of a 3D reconstructionof at least part of a first real object, the computer readable codeexecutable by a processor to: obtain, from a first camera system, afirst image comprising at least part of a first real object; obtain,from a second camera system, a second image comprising at least part ofa second real object associated with known geometric properties, whereinthe first camera system and the second camera system have a knownspatial relationship; determine a scale of at least part of the secondreal object based on the second image and the known geometric propertiesof the at least part of the second real object; determine a pose of thesecond camera system based on the determined scale of the at least partof the second real object; determine a pose of the first camera systemaccording to the pose of the second camera system and the known spatialrelationship; and determine, based on the pose of the first camerasystem and the scale of the at least part of the second real object,spatial coordinates of a 3D reconstruction of the first real object. 9.The non-transitory computer readable medium of claim 8, wherein thesecond real object comprises at least part of a human face.
 10. Thenon-transitory computer readable medium of claim 9, wherein the scale ofat least part of the human face is determined based on a generic facemodel.
 11. The non-transitory computer readable medium of claim 9,wherein the human face belongs to a particular user, and wherein thescale of the at least part of the human face is determined based on auser-specific face model.
 12. The non-transitory computer readablemedium of claim 8, wherein the first camera system and the second camerasystem are comprised in an electronic device.
 13. The non-transitorycomputer readable medium of claim 8, wherein a frustum of the firstcamera system and a frustum of the second camera system do not overlap.14. The non-transitory computer readable medium of claim 8, furthercomprising computer readable code to: superimpose a virtual object at ascale consistent with the first real object according to the determinedspatial coordinates of the 3D reconstruction of the first real object.15. A system for determining spatial coordinates of a 3D reconstructionof at least part of a first real object, comprising: a first camerasystem; a second camera system having a known spatial relationship withthe first camera system; one or more processors; and a computer readablemedium comprising computer readable code executable by the one or moreprocessors to: obtain, from the first camera system, a first imagecomprising at least part of a first real object; obtain, from the secondcamera system, a second image comprising at least part of a second realobject associated with known geometric properties; determine a scale ofat least part of the second real object based on the second image andthe known geometric properties of the at least part of the second realobject; determine a pose of the second camera system based on thedetermined scale of the at least part of the second real object;determine a pose of the first camera system according to the pose of thesecond camera system and the known spatial relationship; and determine,based on the pose of the first camera system and the scale of the atleast part of the second real object, spatial coordinates of a 3Dreconstruction of the first real object.
 16. The system of claim 15,wherein the second real object comprises at least part of a human face.17. The system of claim 16, wherein the scale of at least part of thehuman face is determined based on a generic face model.
 18. The systemof claim 16, wherein the human face belongs to a particular user, andwherein the scale of the at least part of the human face is determinedbased on a user-specific face model.
 19. The system of claim 15, whereina frustum of the first camera system and a frustum of the second camerasystem do not overlap.
 20. The system of claim 15, further comprisingcomputer readable code to: superimpose a virtual object at a scaleconsistent with the first real object according to the determinedspatial coordinates of the 3D reconstruction of the first real object.